{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practice Lab: Neural Networks for Handwritten Digit Recognition, Binary\n",
    "\n",
    "In this exercise, you will use a neural network to recognize the hand-written digits zero and one.\n",
    "\n",
    "\n",
    "# Outline\n",
    "- [ 1 - Packages ](#1)\n",
    "- [ 2 - Neural Networks](#2)\n",
    "  - [ 2.1 Problem Statement](#2.1)\n",
    "  - [ 2.2 Dataset](#2.2)\n",
    "  - [ 2.3 Model representation](#2.3)\n",
    "  - [ 2.4 Tensorflow Model Implementation](#2.4)\n",
    "    - [ Exercise 1](#ex01)\n",
    "  - [ 2.5 NumPy Model Implementation (Forward Prop in NumPy)](#2.5)\n",
    "    - [ Exercise 2](#ex02)\n",
    "  - [ 2.6 Vectorized NumPy Model Implementation (Optional)](#2.6)\n",
    "    - [ Exercise 3](#ex03)\n",
    "  - [ 2.7 Congratulations!](#2.7)\n",
    "  - [ 2.8 NumPy Broadcasting Tutorial (Optional)](#2.8)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a name=\"1\"></a>\n",
    "## 1 - Packages \n",
    "\n",
    "First, let's run the cell below to import all the packages that you will need during this assignment.\n",
    "- [numpy](https://numpy.org/) is the fundamental package for scientific computing with Python.\n",
    "- [matplotlib](http://matplotlib.org) is a popular library to plot graphs in Python.\n",
    "- [tensorflow](https://www.tensorflow.org/) a popular platform for machine learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "import matplotlib.pyplot as plt\n",
    "from autils import *\n",
    "%matplotlib inline\n",
    "\n",
    "import logging\n",
    "logging.getLogger(\"tensorflow\").setLevel(logging.ERROR)\n",
    "tf.autograph.set_verbosity(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Tensorflow and Keras**  \n",
    "Tensorflow is a machine learning package developed by Google. In 2019, Google integrated Keras into Tensorflow and released Tensorflow 2.0. Keras is a framework developed independently by François Chollet that creates a simple, layer-centric interface to Tensorflow. This course will be using the Keras interface. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a name=\"2\"></a>\n",
    "## 2 - Neural Networks\n",
    "\n",
    "In Course 1, you implemented logistic regression. This was extended to handle non-linear boundaries using polynomial regression. For even more complex scenarios such as image recognition, neural networks are preferred.\n",
    "\n",
    "<a name=\"2.1\"></a>\n",
    "### 2.1 Problem Statement\n",
    "\n",
    "In this exercise, you will use a neural network to recognize two handwritten digits, zero and one. This is a binary classification task. Automated handwritten digit recognition is widely used today - from recognizing zip codes (postal codes) on mail envelopes to recognizing amounts written on bank checks. You will extend this network to recognize all 10 digits (0-9) in a future assignment. \n",
    "\n",
    "This exercise will show you how the methods you have learned can be used for this classification task.\n",
    "\n",
    "<a name=\"2.2\"></a>\n",
    "### 2.2 Dataset\n",
    "\n",
    "You will start by loading the dataset for this task. \n",
    "- The `load_data()` function shown below loads the data into variables `X` and `y`\n",
    "\n",
    "\n",
    "- The data set contains 1000 training examples of handwritten digits $^1$, here limited to zero and one.  \n",
    "\n",
    "    - Each training example is a 20-pixel x 20-pixel grayscale image of the digit. \n",
    "        - Each pixel is represented by a floating-point number indicating the grayscale intensity at that location. \n",
    "        - The 20 by 20 grid of pixels is “unrolled” into a 400-dimensional vector. \n",
    "        - Each training example becomes a single row in our data matrix `X`. \n",
    "        - This gives us a 1000 x 400 matrix `X` where every row is a training example of a handwritten digit image.\n",
    "\n",
    "$$X = \n",
    "\\left(\\begin{array}{cc} \n",
    "--- (x^{(1)}) --- \\\\\n",
    "--- (x^{(2)}) --- \\\\\n",
    "\\vdots \\\\ \n",
    "--- (x^{(m)}) --- \n",
    "\\end{array}\\right)$$ \n",
    "\n",
    "- The second part of the training set is a 1000 x 1 dimensional vector `y` that contains labels for the training set\n",
    "    - `y = 0` if the image is of the digit `0`, `y = 1` if the image is of the digit `1`.\n",
    "\n",
    "$^1$<sub> This is a subset of the MNIST handwritten digit dataset (http://yann.lecun.com/exdb/mnist/)</sub>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load dataset\n",
    "X, y = load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"toc_89367_2.2.1\"></a>\n",
    "#### 2.2.1 View the variables\n",
    "Let's get more familiar with your dataset.  \n",
    "- A good place to start is to print out each variable and see what it contains.\n",
    "\n",
    "The code below prints elements of the variables `X` and `y`.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first element of X is:  [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  8.56059680e-06\n",
      "  1.94035948e-06 -7.37438725e-04 -8.13403799e-03 -1.86104473e-02\n",
      " -1.87412865e-02 -1.87572508e-02 -1.90963542e-02 -1.64039011e-02\n",
      " -3.78191381e-03  3.30347316e-04  1.27655229e-05  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  1.16421569e-04  1.20052179e-04\n",
      " -1.40444581e-02 -2.84542484e-02  8.03826593e-02  2.66540339e-01\n",
      "  2.73853746e-01  2.78729541e-01  2.74293607e-01  2.24676403e-01\n",
      "  2.77562977e-02 -7.06315478e-03  2.34715414e-04  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  1.28335523e-17 -3.26286765e-04 -1.38651604e-02\n",
      "  8.15651552e-02  3.82800381e-01  8.57849775e-01  1.00109761e+00\n",
      "  9.69710638e-01  9.30928598e-01  1.00383757e+00  9.64157356e-01\n",
      "  4.49256553e-01 -5.60408259e-03 -3.78319036e-03  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  5.10620915e-06\n",
      "  4.36410675e-04 -3.95509940e-03 -2.68537241e-02  1.00755014e-01\n",
      "  6.42031710e-01  1.03136838e+00  8.50968614e-01  5.43122379e-01\n",
      "  3.42599738e-01  2.68918777e-01  6.68374643e-01  1.01256958e+00\n",
      "  9.03795598e-01  1.04481574e-01 -1.66424973e-02  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  2.59875260e-05\n",
      " -3.10606987e-03  7.52456076e-03  1.77539831e-01  7.92890120e-01\n",
      "  9.65626503e-01  4.63166079e-01  6.91720680e-02 -3.64100526e-03\n",
      " -4.12180405e-02 -5.01900656e-02  1.56102907e-01  9.01762651e-01\n",
      "  1.04748346e+00  1.51055252e-01 -2.16044665e-02  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  5.87012352e-05 -6.40931373e-04\n",
      " -3.23305249e-02  2.78203465e-01  9.36720163e-01  1.04320956e+00\n",
      "  5.98003217e-01 -3.59409041e-03 -2.16751770e-02 -4.81021923e-03\n",
      "  6.16566793e-05 -1.23773318e-02  1.55477482e-01  9.14867477e-01\n",
      "  9.20401348e-01  1.09173902e-01 -1.71058007e-02  0.00000000e+00\n",
      "  0.00000000e+00  1.56250000e-04 -4.27724104e-04 -2.51466503e-02\n",
      "  1.30532561e-01  7.81664862e-01  1.02836583e+00  7.57137601e-01\n",
      "  2.84667194e-01  4.86865128e-03 -3.18688725e-03  0.00000000e+00\n",
      "  8.36492601e-04 -3.70751123e-02  4.52644165e-01  1.03180133e+00\n",
      "  5.39028101e-01 -2.43742611e-03 -4.80290033e-03  0.00000000e+00\n",
      "  0.00000000e+00 -7.03635621e-04 -1.27262443e-02  1.61706648e-01\n",
      "  7.79865383e-01  1.03676705e+00  8.04490400e-01  1.60586724e-01\n",
      " -1.38173339e-02  2.14879493e-03 -2.12622549e-04  2.04248366e-04\n",
      " -6.85907627e-03  4.31712963e-04  7.20680947e-01  8.48136063e-01\n",
      "  1.51383408e-01 -2.28404366e-02  1.98971950e-04  0.00000000e+00\n",
      "  0.00000000e+00 -9.40410539e-03  3.74520505e-02  6.94389110e-01\n",
      "  1.02844844e+00  1.01648066e+00  8.80488426e-01  3.92123945e-01\n",
      " -1.74122413e-02 -1.20098039e-04  5.55215142e-05 -2.23907271e-03\n",
      " -2.76068376e-02  3.68645493e-01  9.36411169e-01  4.59006723e-01\n",
      " -4.24701797e-02  1.17356610e-03  1.88929739e-05  0.00000000e+00\n",
      "  0.00000000e+00 -1.93511951e-02  1.29999794e-01  9.79821705e-01\n",
      "  9.41862388e-01  7.75147704e-01  8.73632241e-01  2.12778350e-01\n",
      " -1.72353349e-02  0.00000000e+00  1.09937426e-03 -2.61793751e-02\n",
      "  1.22872879e-01  8.30812662e-01  7.26501773e-01  5.24441863e-02\n",
      " -6.18971913e-03  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00 -9.36563862e-03  3.68349741e-02  6.99079299e-01\n",
      "  1.00293583e+00  6.05704402e-01  3.27299224e-01 -3.22099249e-02\n",
      " -4.83053002e-02 -4.34069138e-02 -5.75151144e-02  9.55674190e-02\n",
      "  7.26512627e-01  6.95366966e-01  1.47114481e-01 -1.20048679e-02\n",
      " -3.02798203e-04  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00 -6.76572712e-04 -6.51415556e-03  1.17339359e-01\n",
      "  4.21948410e-01  9.93210937e-01  8.82013974e-01  7.45758734e-01\n",
      "  7.23874268e-01  7.23341725e-01  7.20020340e-01  8.45324959e-01\n",
      "  8.31859739e-01  6.88831870e-02 -2.77765012e-02  3.59136710e-04\n",
      "  7.14869281e-05  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  1.53186275e-04  3.17353553e-04 -2.29167177e-02\n",
      " -4.14402914e-03  3.87038450e-01  5.04583435e-01  7.74885876e-01\n",
      "  9.90037446e-01  1.00769478e+00  1.00851440e+00  7.37905042e-01\n",
      "  2.15455291e-01 -2.69624864e-02  1.32506127e-03  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  2.36366422e-04\n",
      " -2.26031454e-03 -2.51994485e-02 -3.73889910e-02  6.62121228e-02\n",
      "  2.91134498e-01  3.23055726e-01  3.06260315e-01  8.76070942e-02\n",
      " -2.50581917e-02  2.37438725e-04  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  6.20939216e-18  6.72618320e-04 -1.13151411e-02\n",
      " -3.54641066e-02 -3.88214912e-02 -3.71077412e-02 -1.33524928e-02\n",
      "  9.90964718e-04  4.89176960e-05  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n"
     ]
    }
   ],
   "source": [
    "print ('The first element of X is: ', X[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first element of y is:  0\n",
      "The last element of y is:  1\n"
     ]
    }
   ],
   "source": [
    "print ('The first element of y is: ', y[0,0])\n",
    "print ('The last element of y is: ', y[-1,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"toc_89367_2.2.2\"></a>\n",
    "#### 2.2.2 Check the dimensions of your variables\n",
    "\n",
    "Another way to get familiar with your data is to view its dimensions. Please print the shape of `X` and `y` and see how many training examples you have in your dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of X is: (1000, 400)\n",
      "The shape of y is: (1000, 1)\n"
     ]
    }
   ],
   "source": [
    "print ('The shape of X is: ' + str(X.shape))\n",
    "print ('The shape of y is: ' + str(y.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"toc_89367_2.2.3\"></a>\n",
    "#### 2.2.3 Visualizing the Data\n",
    "\n",
    "You will begin by visualizing a subset of the training set. \n",
    "- In the cell below, the code randomly selects 64 rows from `X`, maps each row back to a 20 pixel by 20 pixel grayscale image and displays the images together. \n",
    "- The label for each image is displayed above the image "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QI6c8roez+FBVKpVwuVywWq2Yn5+H3W6H3++Hz+fjLKiYvEoXgo2NDVgsFhwcHCCXy6FaraJcLjN3aNJAn7dSqeSg7+rVq3C5XLhw4QIMBgOXtInPOOnlJLosULbTbrdjenoaly9f5oYMpVLJvoxK5P1+H71eD41GA5lMBvl8Hq1Wi334y/ij15aRAR4fzhqNBhaLBUajkVNJ7XYbnU4HOzs7vOgrlcoh8p3D4YBWq8XMzAwf1EtLS5DJZEin08hkMvjyyy/RbrcP1dfGDfThEwnK6/Xiww8/hNfrRSgUgl6v5+eNRCK4desWyuUy4vE4lwCo1AQ8rk3WajXUarWx7UARHwoUlM3NzeHDDz+Ey+XC0tISrymZTIZ4PI54PH6o9bzVakGhUODatWtclguFQkin0/D7/SgWiwiHw+h2u2PhYJ43gAEeH0YqlQorKyvweDy4cOECLBYL8vk87t69i3K5jEePHgEAlpeXEQqF4PF4cPnyZcjlcjx69Gjs1szTQJcjvV4Pp9MJp9OJ7373u3C73VhaWoLX64UgCMhkMrxuBEFg4Um6QXY6HS55t9ttDIdDmEwmLmeq1WrOfJ41XyTOgoxmYtRqNV8S5ubmYLfbcfPmTeYw6vV6GI1GGAyGQ6XtSqXC9ur1erDZbLh69SpCoRCmpqaQy+Xw2WefIZ/PI5lMolqtTqRAp8ViwfT0NKanpzE7Owur1crl7FKphGq1ilwuh1KphGazeajrdhIg9hN0buv1enz44YeYnZ3FhQsXMD8/D71eD+Dx+dRoNDAcDjm4K5VKyGazePToEX79618jn89zo8rLlNOBN8CRISIqBTIAUKvVUK/X8ejRI+zu7iKbzSKTyfD36HQ6hEIh7jgxGo18M1Cr1SiVSjg4OMC9e/c4AhzXhUEiQDqdDvPz85iZmeFAhp6rWCzi/v37WFtbw89+9jMWd1OpVPB4PFzXp1RdtVpFvV4/RD4cN5CjJX7H/Pw8vv/978NmsyEYDLJuTL/fRzKZxK1bt5DJZLCzs4NOp4N2uw2NRgO9Xg+VSsV8mlQqhUAgAIVCgXg8PhZp3ucJKsS3IeoSWF5exvLyMhYXF2GxWBCJRPDZZ5+hUChgb28PCoUCP/7xj5kDsrKygnq9zrYdZ66MuDNJLpcz4TAYDOLjjz+G1+uFzWaDVqtFKpVCOp1Go9FAsVjk9n2tVsu+K5/Po1qtHiKOm0wmdDodDmTE5aWzbDMx4VmpVMJoNGJ6ehofffQR3G43PvjgAyZiKpXKbyiwk9YH3Zr7/T7cbjdcLhe63S7eeustJBIJVKtVLhOUy2UAk9WgATwOZKampjAzM4PZ2VnuzO33+3xA5/N5VCoVbrw4y2vjRTAaFCsUCng8HjidTnz44Ye4evUq7HY7bDYbn00UyBAVQKlUolwu4+DgAA8fPsSvf/1rNJtNPvteNuh7LRkZ4MlN2mKxwGazwWAwAAA6nQ4ymQyKxSL29/exu7vLnRMEut2YTCYEg0EA4PSmSqWC2WyGyWRiDsC4kDbFIOdgNBqh1+sxOzuL5eVl+Hw+Tu/X63V0Oh1EIhGsra0hEolwzZ64HzqdDkajEWq1GsCT7p5erzcW5RMxxAc23aSdTicWFhYwPT3N5PBarYZyuYxMJoNqtYoHDx5gY2MDpVIJiUSCn1uj0SAWi8FsNkOr1cLr9UKj0cBms/FmmZTMA/CE40BZzPn5eUxNTaHdbuPhw4fY3NxELBZDtVpFq9WCTqdDs9lEvV7noJFGX4zDejkO9Lnq9XqYTCZMTU2xOCIFMPV6HdVqFTs7O9ja2kK9XkehUOAWWrVazXwaarN2Op1cWlKpVOyDzuplalSvhMqtYm4dyTrQpSiVSqFcLsPpdHLWUxAEJjhXKhVsbGzwZWk4HHIgYzab4fV6odVqsbS0BJvNhkwmwyUoIryeRVu9KMREeqPRyI0E3W4X7XYbkUgEkUgEqVQKzWZzYqQwxBgOh5yssFgsWF1dhcvlgsfj4SweADSbTS4ZkSCpUqlk2gTxrqi8+6p4pUBG3CJLEuhTU1OYnp7mlGStVsPGxgaSySS++OILrK2tod1uM99FzAInnkcqlcJ7770Hn88HjUYDn8+HZrPJ7ZPiuTDjsFCoXXEwGHCKe3V1FT/60Y9YGwUAstksCoUCvv76a/ziF79AvV5HpVLhkh3VISkFLAgCz6kg/ky73X5ti+MkQE6XFvbCwgK+973vYXZ2FtPT0+j1ehz8fvHFFwiHw9jc3GTOEEX7/X4fGo0G9+7dQ6PRgMlkwvLyMoxGI0KhEN8gxrH0JoY4oyQIAoxGI7797W8jFArhgw8+QCgUwm9+8xv8/ve/x8HBAe7cuYN+v88zqKrVKgqFApc3ifMxDvvoOJBdbDYbFhYWcOXKFfzlX/4lTCYTnE4nAGBnZwfZbJbtQ51bgiBwJi8YDMJiscBgMMBoNOLixYuYnZ1lLhq9blSj6CzgqM+Q1v3S0hKmpqbw7rvv4t133+Xgny4G3W4Xq6urHJRotVpEIhH8/ve/RyqVwqeffopiscj7hzpSr127hn/1r/4VTCYT/vRP/xTVapUP8mw2i2w2OxGBMvDYvlRa8ng8zP2kDNTXX3+Nu3fvYnt7G+VyeSLLSsPhkKU//H4//vzP/5w7/EwmE18Wi8UiHj58yMGwTCbjzmXiv9KvUamMl8ErBTJEMKShdBTR062FUpo04LDT6XDmQNxVQwcylVUymQyzmAFwuclqtfKiabVar/LWTxxEljObzXC73XA4HDCZTCy21e/3kU6nkUqlePSAuGZIwQml58iJEoHqVfvwTwv0fDRigFrQDQYDj65Ip9PIZrOIx+NIJBIoFAqcqRLP76IyW7FYRK1W4zVC7f7EDziqq2LcoFKpmIxJIlOU3s7lckgmkygUCryGaH3QASZeU+OM0RZri8XC+4vK27RWkskkEokEUqkU8vk8ms0mz+Pq9XrMNWo0GrDb7QDA0g9HlUjO8l4bDocsXaDT6ZiDR+JtjUaDMzHRaBSdTgcWiwW9Xg9qtZoDGepMKhaLKJfLh0ivw+GQbUp+TSyT0Gq1kM1mT9sUrwQxkV4ul3PzirgyIC6h1Go15lQB4+tfxKDPnAIPh8PB/E7q7KNMS7vdZrHNaDTKWjrENaOz/nXTIF46kKGNQqWSa9eu4cKFCyx+Q9ka8TA6sdCXuKZNr+12u9jd3UW5XEYgEMCFCxegVCq5VfLmzZtwu924desW4vE4gLO/UChAs1gsUKvVuHTpEj788EP4/X7Y7XYMBgPE43GUSiX8/Oc/x/r6OnZ2dlCtVgE8qfnTLyLgUaDY7/eZTA2cfXsQxPVWhULBG+PatWu4evUqAPCB/I//+I+Ix+N4+PAh0uk0l9IAsEOlzGAikUCr1cL8/DwWFxfRarUwPT0NuVwOj8dzqDNlHG9LFJQ4HA68++678Pv9eP/992E2m3Hv3j2kUil89tln+MMf/sD8DrlczlkrymKJJ9COM2h/UTlxZWUF3/ve9+Dz+WC1WrnMls/n8atf/Qo7OzuIxWKHyPMAeD0Ui0XIZDLMzMyg1+thamqKSy10ISOfdlZB+0Gn03Hq/yc/+QlWVlZQLpcRiUSwvr6OX/ziF6hUKkilUuj3+1hbW4PD4eAAN5PJYH19nQ8ncUdSq9VCIpFApVJBp9NBKBTCX//1X8NqteK9995DKBTCT3/6U+zt7Y3lGqMghd672WyGXq+Hw+GA1WrlM24wGHDmvFQqsTL9JIGCDhqdc/XqVfz1X/81TCYTbDYbd8sC4Iz5xsYGfvGLX7Aittlsxve+9z0oFAp0Oh2USiXUarXXtpdeKZARBIGjd2K9GwwGbmlstVpoNBqsASNOiR+VdhsOhyzsVq1W0Wg0eJox6YrUajU22lmHOCqndlcqDZlMJigUCrRaLZTLZWb7x2IxVCqVb/TT05/FGZnRQOZlGd+nCXq/ZrOZZfSpO6RUKqFcLiOVSvHMpHK5zIqjYuE4snWj0YBMJmMCNPA4I0Ots8SVGDeOFXBY5sBkMnGLI9WmS6USYrEYMpkMSqUSgKNnvIzbcx8HsRAXOVbibpAfKhQKvIbi8TiKxSJ3K42OgiACL0nMi9cJ3bzpNWftgBa/HxK2czgcXAZyOByoVCooFArIZDIIh8OoVCooFosYDh9rpFA3pEwmQz6fRz6f5yy62J+RHYrFIqLRKJRKJZrNJsxmM8xmM/r9PoxG46nY4XVglL+n1+thNptZsoAyu4PBAM1mE41G4xvjKyYBo2cY6U+RjAOdxZS0KBaLSCQS/KvX63HTD2nH0UVUTJZ/1b30UoEMRaJKpRLBYBBOpxOXLl3C6uoqDAYDut0uYrEYbt++jUwmg3/6p39CNptFMpnkhxk9pKlOX6/X0e/3kcvlkMlkmFhGeisqlQoPHjzgW8RZFn6jZ9RoNHjvvfewuLiI69evY2FhAQD4GSnjcP/+fcTjcXQ6nUNaFRQwUiBE9dl2u418Po+1tTWkUikOFs86xO+RuAfLy8u4evUqZmZmoNFokE6n8fXXXyMajWJ9fZ2f72nkVDqQisUiKpUKC3bZbDZMTU2h3+/D6/Wi0+mgUqkcyniddYi7TgKBAPx+Py5fvowf/ehHkMvliEajqFar+Oyzz7CxsYFcLncowKP1QzdK4pyNs2K2+Llo6vnU1BSuXbuG2dlZAI/XQiqVwueff45YLIbt7W0kk0n0er1DCqPAk4CP1oPdbsfc3BwTxjudDlKpFFKpFA9upRLdWfE9tE5ozk0gEMDHH3+MQCAAg8HA/LtPPvmEAzsKSAAgkUgcKgWR6upRFySxv97c3ESj0cCDBw9QKpVgMBjg9Xq5BXdcQW35Wq0WN27cwPz8PN566y0sLS3xLLdyuYz79+/z+qLRFWeVDP68oEuyIAjcaHP9+nV873vfY6VeMdl5fX0d6XQan376KT799FNu6KF102q1kEqlEA6HUa1WuVQnjgFOnCNDDpJIviSr7/F4OOIqlUrY3t5GIpHA5uYmisXiodvM6IdMf6fULWmjmEwmXlCUKqY2QapPnsUFI978SqUSU1NTWF1dxfT0NJxOJ5Og8vk8NjY2EIlEkEwmUS6XObUrvhFS/Z74QiR8V6vVkEqlkM1mWXhonEDtw263m7tvKFNFwQjZ6WlTh8nOw+GQyb/lchmlUolne1Hnm16v57LcOIHeL3X2zczM4MKFC2i1Wjg4OEAikcDBwQF2d3cPda3R+qGuN4PBAJPJhH6/z5mpcbMF8CQ7pVQquSFgbm4OPp8PNpuN53CRftDBwQGy2SwqlcohgbdRkN0oA0yZUwqAi8Uid6Scte5ACmQUCgXMZjMcDgfm5+cRCARYgCwSieDu3bs8lgF4MqKgUqkcCmwp+wfgUCAj9m0k2qlWq5FKpaBWq3mWHt3ExxWUAVer1Ux1mJ6ehsvl4td0Oh0kEglEIhHkcjmUy+VD62sc9xaB3jt9pn6/H8vLyzCbzeyLac2l02ns7e1hc3MTjx494uylQqHgP9dqNc6GElXideGlAhlKyZLEvjg6I42YR48e4fbt20wSIwLU0z5YcfqWDLO2toZ+v48LFy4wJ2c0kjuLEPfZW61W2Gw2hEIhTE9Pw2QyodfrIZ/P4/79+4hGozg4OOCMw2gkTySr2dlZThFTazuV74rFIqrV6plzrMeBDiJqaaV11O12kU6nEY1GsbW1hXQ6jW63+8wbjrj7CQDf1CloEWc1xsm5kFaM1WqFRqPB8vIyPvjgg0Nkytu3byMcDiObzR5SdKYAxmAwsDqy0+mEQqHgAJqInuOkBC12sDMzM7Db7VhdXcXy8jJsNhtarRYikQg+//xzJBIJ7O3tIZfLsWM9KgtHe4da/ufm5rCysgKHw4Fms4lCoYCtrS1EIhGUSiXOxgBnJxtMl0jxYEyn0wmTyYSHDx8ilUohEomgXC4fEoak9z+6x571XGLiZqfTOZTlOys2eRmQr9BoNJifn+eKw/LyMvMam80mqtUq4vE49vf3WSpj3DMxBPHl+cKFCwgEAlhYWIDT6eRsFPnqcrmMW7duYW1tDeFwmLN4ZAtxIFOpVGAwGGCz2ZBMJnlEyKuW416aI0OBjFar5QyBIAjI5/Mcld25c+fQjJvnCUCofpZOp7G5ucnSz9Q5INZwOMsLht6vw+GA2+1GMBhk4mC/30c+n8eDBw8Qi8UQDoeRz+ePdLLD4WM1ztnZWQQCAe7oIafRbDZRKpX4NnWWbTIK8WahYaLtdhvVahWxWAxbW1t8aBznIMSBCQW6Ys0PcRAzThglG1qtViwtLeH999/nkR+5XA537tzB3t7eoTUg3h8GgwGrq6uYmpqC0+mEUqlEPp/H+vr6oUDmrO+pUajVakxPT8Pn82FlZQUrKyusyBuJRPDJJ58gm81if38fjUaDb9ijoPUhl8tht9s5k7G0tMTZwXw+j62tLezv76NQKHBQdJYE36gcoFar4fF44PV64XA4YDabkUwm8fDhQ4TDYSY0j3INn/X5j/JGgCfjNMSBzDgFxUeB1oNKpcLCwgICgQAuXbqEpaUlAOBAJpVKIR6P4+DgANFo9FAgM06XpVGIqwBarRYXLlzA8vIyFhYW4HA4eJ10u13E43GmAVCmj6ok4kCGBtVWKhVMTU0hGAxie3ubpQxOPJChSIsmDdOAMRqkVq1WkU6n+QB6GQf5rNee9UVC70+pVPJcF4PBAIVCwSq9iUQC+/v7yGQyh2539P2U0TEYDHA4HAiFQggGg6wfQ6nzUql0KKIdl8OI1hGVOnQ6HTQaDWq1GgqFAsrlMur1OprN5gsRc8mZU0s2AF6vo1OwxwF0+IZCIR52aDKZUC6Xsb+/j3A4zHIE4gNkOBxCp9PB4XAgEAhgdnYWfr8fAFhcMBqNIp/Pj42ir3hf6fV6uFwuzM/Pw+/3M7GUSPPhcJhJz087WMUlciKCz83N8V4zGo2H9FCoVDUOHAha67QfqtUqt5yP8hOB5/ep4gBG3OJNauxWqxXVapU1aiYFZDO6aNNFIBaLcUnprNIcXgQUxFEwbLFYMDMzg5mZGRaGbLVaTBonfmahUGAfJCbQC4LAl1QiglOr9uu01QsFMuKNT3LeU1NTmJqagkaj4VQTpayJwf0ipSAx6Y60IcSb7XWxnN80hsPHCogkROVyuaDVahGLxbCzs4N79+7h888/Z9VV8QFLh7DRaEQgEMD09DTeeustnlxMma+trS3EYjFmzJ91mxDEgZrL5YLb7YbdbofZbEYkEkE4HEY8Hkc+n2fOy/OuHwqcxSrHtJZGy5Jn2V5ieQK1Wo233noLb731FhYXF+HxeJBOp/Gb3/wGiUTiqdwqi8WCGzduYHp6Gh988AFcLheSySTi8Ti2trZw584dVmsdB9C6oZEmU1NT+M53voNAIMCy6Ht7e/jiiy+wtbWF7e3tp07UpX3W7/ehUqn4ECapdcpeRaNR7O7uYn9/H3t7e4jH4+j3+8wrOYsQ+07KmBCHgbr+Rm3xPBj9HhKhtFgs8Hg8mJ2dhdfrxe7uLgeU4xAgHwdxwEdZ0GaziYODA/z85z9HOp3G7u4uqtXqmRRJfFHQ56pSqVj07t1338Xq6ipnt2mIcTwex89+9jMkk0nWjKG1R3tLLpfDZrPB4XDA6/Vyt9PrvnC/dGmJ3ijNuSFuS7fbZYXZlyES0oIhGWSdTscb8qibxFmDOAijYI8Ig6Q5QDe7RqPBdhJnCejAtVgsCAaD8Pv9rGNALaWkoFkqlbgmeZYP5qdhNMCgW6RY5O95QK8jZyLWNqD2SJICOOtlFHGgR8rPDoeD+S2NRgPlchm5XI7LHPQ9VIqk1lu/3w+32w2VSoXhcIh8Ps+CcFT2JZxVexDo/Wk0Gi4BUUssDVItlUqciaGum6cFruKxH9Se7HQ6mXBOPy8Wi7Fa7bgEfcDh5z1uPx2XoRT/2+jvgvBYyNLlcrHNBoMBCoUC4vH4IXXbcQQd6uIML5W+SXhydP+N67MSiFdHe4yoDGq1ms/8ZrOJTCbDk6upKiD2QQS5XM5lcaPRCJ1Ox11MZLfXgRcKZMTpNeJm7OzsoNVqwePxwGw2o9Vqsarqi4jdiNPGCoUCU1NTuHHjBkKhEM8VGs3QnDXQYqf2R6/Xy/NvVCoVms0m9vf38Yc//AEHBweo1WrM/wDA9vJ4PPB4PFheXsZPfvITOBwOzM3NQa/Xo9VqoVqtYn19Hb/+9a+RTCZfKvN12qBNT2qR9LnK5XKo1WrmXBGh8FmgQJdKVDTzazgc4uDggDNhVMt+GunzLGA4fDzSwmAw4O2334bf78eNGzewvLyMdDqNu3fv4s6dOzwZncpvtP5CoRBmZmawsrKCn/zkJyzelclk8Otf/xr379/H7u7uNzg1ZxXi7BQJG77//vvw+/0c5EejUZRKJaytreGrr75i5yoeMksHOWWuSM7A7Xbj+9//PjweD65du4ZgMIhUKoV79+7h7t27+Lu/+zsUi0WUSqUjW5HPGkaHPgJPOI0vUlY96hnJR1E3TzAYxJ/+6Z/CZrNBLpejWCzij3/8I+7du3eo/XYcQSN2SNOMRAD39/fx8OFDPHz4kDOaZzlD9zygtUK8GLfbjevXr/PMO2qz7nQ6iMViPLpif3+fA1aFQsHJDbqU6nQ6rKyscKelx+PhrDB12r4OTtUr6cj0ej2+7YoFk14FR2VkxAfaOMzKoXKARqNheXzKpNCgumq1yk5BfIhTlwkJDwUCAVgsFp7rQSKDxWIR2WwWpVJpLGzyLFC0T51MpDxLRNTjno8OF9o4ZHMqd5bLZZTLZXZI40KKpqnMJPBGXV10EyyVStxCLVZ/tlgsLJbndDo5iKYsXiwW40wecPYzMcDhz1ir1XIpSKPR8Dqp1WqsX0HBHXC4PCAmmBuNRrhcLpbUpyGKJOFPI0OSySRqtdpYZWPEiuqU3VOpVN8gOz9rX42CPgOxECpxAIlonc/nmfs3jhCfN+RvKaAplUqchaAL+1m+XD8vxBklUo+nwZA0CJLGMBDHLpfLoV6vo91uf2P+GK034k45HA6e99bpdHiMzOs6u165tETDCoHH2RSLxcK6Bbu7u7yYn5V2Ew+BpMNMp9NxNqbT6bCAHM0hohLBWYG4+4FIrKQoSyW4fD7PAQgFf/ShT01NwWq14saNG7h+/TrcbjdmZ2c5zd1qtfD5558jHA7zAEWSVh+nbAzwJGVN3Q5EPlWpVLh48SLq9Trm5+eRy+VwcHCAdrvNzzga2JK9dTodPv74Y8zOzjLPgcbFx+Nx5HI5VKtVvpWfRdB7I0cyNzfHHLRWq4WdnR389re/xf7+Prfc0/iL2dlZ2O123Lx5E2+99RbPGiqVSvjtb3+LVCqFO3fuYH9/f6xGWohvi5TtJD0m0lMqFApIJpM8eJVueeJLAqXIqYPQ5/Ph8uXLMJvNmJmZgVarRblcRjqdxj/90z/h008/ZQ0jSoOf5QwD+c5er8fBe6vVglarRSgUQr1eR6PRwMHBAZdNgCeikKPPNVomEIQno1aWlpZw4cIFFiGsVqu8xiKRyCGF4HEEzQ3a2NhALBaDwWBgYivJQ5xlMdaXAYncUgBD5xeVDLe3t3H//n08evQI9+/f57lSAFip1+FwcOBCkiHf//734XA4oFKpkMlkcO/ePXz66afY29s7VK59FTu+UkaGsjJUh5bL5dDpdDw9llJNz/PzxLcnSoNSlAeABbwqlcohYb2zBjqIqP2XMgvtdvvQYDGSQCe7qVQquN1u+Hw+LC8v48aNG5z67vV6LOa1u7uL9fV1RCIRlhYHxncz0Ywtuu1YrVauzTocDgwGA0QiEbaV2DGKA0dSPp6fn8fly5cxPT0Nq9WKcDiMZDKJVCrFweDThPVOG2IyPQm9kS3owM7n89jf30cqlWIFaMpCTU1Nwe/3Y2VlBdevX2cRtGazia2tLRwcHLBE/+sWpDoJEKdFLFZHolzNZhOVSoUPa9pX4lllpFUUDAZx8eJFTE9P491332X7kX5VOp3G1tYWbt++jWazyaMuxqF8QAE+tUJTec1iscDn87HAKPCkHDvK0RND/DXKeBqNRkxNTeHKlStwu93wer0YDoeIRqPY399HsVhEp9M5xH07i776KIj9ab/fZ77V2toaCyLSL3HJexwaCI6D2PdoNBoe4ksdRoPBALlcDpubm9jb20MqlTo0r46+X6PRwOl0IhgM4sqVK9xZaDKZWFE9Ho9jc3OTs3ZUlXgVvLQgHm0Y6g0nngYNsbPZbFxXDofDfHCT0cSg2TGkC2G327G4uAin0wm1Wo1arYZcLseCVNQCSe/lLEGckiTSM91+yRFTl4Rer4dWq+UPenV1FV6vF1NTUzyvJJFIoFwu4+7du8jlcvjqq69wcHCAQqHA/9+4Qcy1oucggS4SxwuFQvjOd76DQqEAl8uFarXKWTg6nIlMrdVqmStBEvXVahW3bt3CgwcP8OjRI1aWPuuHkbilVZzVo8MJALRaLWZmZjA/Pw+NRsOS4fPz87Db7TCZTEgkEshkMtjY2EA6ncaDBw+Qy+XQaDTGLnv3NIifgTgbJpOJS3HBYBAajQY2mw1qtRpWqxVarRbT09OYmppi9dler4etrS3UajV8+umnCIfDWFtb43LSqHDcWQUFbe12G8lkEmq1GslkEgB4UnGn0+EsXSKRQLvd5tbh0TZ8pVLJJXKv1wuj0YiLFy/C6XTyqIxKpYL/+//+vzn4y2QyaLfbZ5qDJoY4wzvaSEIXJUEQkEwmucOUfgH4xsVqnEG+RxzEqFQqbuIh8jtlXuRyOYLBIM8502g0zFujjIxWq4VCoeDLVCqVwsbGBuLxOGq12msjSL90aYkO7Gq1Cp1Ox4EMjYs3Go1cX06n06z4eFQwQ1O0TSYTLl26dEhFsNvtMq+EbpV0KJ21W+VobZUCGbGiKGWaDAYDjEYjbDYb3n//fVaPdLlcfJMsFotIJpNIJpP43e9+h0QigQcPHiCdTnO3z7iCAhm62dCBQYFMMBjEd77zHZTLZXg8HlSrVSa25nI55PN52O12zMzMwGQyYXZ2FkajEUtLS3A4HPjiiy9w+/ZtrK2tYW1tjbuVztJ6GQXdasRZFpPJBKPRyIGMIDwe3ubxeHDp0iWYTCZMTU2xroper0c4HOYZVb/85S9Z84LaIyehpi9+/2Lei9lshsvlQjAYxM2bN2E2mzE9PQ2dTgez2QyNRsPdSdRhSeNU0uk0fve732FzcxPVahW1Wo2Dg3HIKlAWqt1uI51OQ61WI5FIQKlUYnZ2FlarlbvZotEobt26xVOtaX+Iy2darRYWi4Xb+GniejAY5P/z9u3b+Md//Edks1lsb2+jXq9/YwTEWbfbqMaUOMtA5f9EInHINqOZ8LP+jM/CURkZEhSlQIYqIoPBAA6HAwaDAe+++y5sNhuCwSDMZjPcbjfPRrTb7TyuqFqtYmtri3/RQEnCq/qjV1L2pYNoOBxia2sLcrkcfr8fXq8XZrMZy8vLcDgcqFarnI6jgIb4LdQ9sLS0BIvFguXlZVakpDollQaI4HiWBamoRl2r1ZgUpdVqmUg4NzeHDz74gOv9er2eRxfIZDIm8hYKBWSzWezs7CCXy7EiJ41FP4vP/qIgR0Hkr3Q6zdO9+/0+DAYD5HI5pqenmVQuHvpoNBp5gKbVaoVarWYS2ubmJmsdUHp9XJwN8RfoFtRoNGCz2TgT02q1YDQa4fP5mIjXarUQDoe5lr29vY1YLIZEIoFarXao5Xxc1w7dkMmpajQa1nSx2Wzo9/tYXV3l9Pbi4iJ0Oh08Hg/UajU75k6ng2QyiUqlglQqhWKxiDt37iCXyyGbzfKhPqphddYhLhm1222USiU8fPgQ+Xyeh6b2+324XC4ONprNJhYWFniYr7jFmkiflCHV6/VoNBpIJBIolUqsJ0K2FIuhEcbBbkajEVqtFk6nE06nE8CTPUjifiT41ul0ODM8jmrhx0EcvFGGDniS6QsEArh+/TparRbK5TJ0Oh0uXLgAo9EIp9PJWWQKphOJBBqNBnZ2drg8Fw6HkcvlDq21U8vIkDPsdrsIh8NIpVIwGo3Y3t7GRx99xB03P/rRj1CpVBAMBpHP5/Hw4UNuuaL6vlKphMPhwA9/+EN4PB7mNlCEXKvVsLGxgXA4jP39faTTaW71OmsgDZROp4NMJgO5XI6trS20223WWvj2t7+N69evc3aGUnk0pyqXy+Hrr7/GrVu3EI/Hce/ePW5ppwj2rJdHngf0/judDhqNBsLhMLRaLav5koYOBccA+HYkVhWl1CeVm27duoVYLIYvv/wSf/zjH5mMTreNs2w38W2PSNCFQgEmk4n1YCwWC27evMnZhE6nw0rIDx48QCaTwZ07d3Dv3j20223uagK+OUtnnECZzm63y3ahVmwayur3+3Hp0iV0Oh3OelKWTxAEPqzD4TBnrb766isUCgXcuXMH1WqVNbHEAxPHCZTppIaIn/70pzCZTCgUCpifn0coFMLCwgK32dJaEx8s4o4n+jOttb29Pezu7nL7MensUKA8TjajNnK32w2n04l3330X77zzDh/mrVYLsViM5S7IP+dyOXQ6nbHtyjoKR1UTKJihy+XVq1exuLj4jcwx6c5QANNut5HL5bC7u4t0Oo1f/epXSKfTzJ8SV2Zelz965WiAbo7FYhEqlYpLIcRfUCqVcLlc0Gg0aDQaPDSRSkM0WNHj8cBms3Gbca1WQ71eRyKR4HkORAw6y5tF3E1DnVY6nQ6lUgk2m40zUGIFRCo/JZNJFAoFRCIRfmYiN5Pg0DgfRqOgDUFcmXg8zk7E4XDwLdpsNh9K75PQmViUMZvN8owmmkTbbDbHztmIb0WtVguZTAZKpZI1cWhtkUx4q9VCKpVCvV5HNBrljBS1949274wjyCb0edfrdaTTaQwGA7jdbvR6PRZAVCgUPHVZPKyu3++zCFc4HMbe3h4SiQRnZCiAHiVwjkNGgSDOHokzCoPBAPF4nLWZiLRrNpsPKUHT99K6ob3V6/VQKpXQbrcRi8WQyWTYbnTBooNtHEF7inwNtfID4OyEWJdnnNbEi4DOLmqjLxaL3LRDDQg0sJgCH0oodLtdjgNIGoQmzlPnH2XUR8tzrwOvHMjQDWBnZwfhcBiNRgOxWAzBYBBvvfUW9Ho9VlZWoFQq8c4773xDhZaMYTQaIZPJUK1Wkc1mcf/+fdy/fx+xWAx37txBrVZDrVYbC6IifVCNRgO3bt3C/v4+BzN+vx/BYJCfodFo4P79+8jn8/j8888RiUQQi8UQj8e57CKuzZ71Z38eiJ+BArkvvvgCt27dgsVigdls5tZYl8uFDz/8EBaLhR1OPp9HLpfjzp5isYjf/e53yOVynJWgkgz9f+NgO3qPVJokCXCTyYR79+7B5XLx60gTptFosMgflW4pMAae3lo7ThDbpd/vY3d3Fz/96U/hcrlQqVTgdDpx4cIFVj4mYbZwOIxKpcJEXupa29nZwfb2NgeDdBkbzSiM84FFAUqlUkGtVsMnn3zC83PcbjdMJhOCwSCTpMXrgw5v0h4SB8tEeqUSC10sjyJtnnX7UWBcqVTYXiaTCZlMhoew7uzs8JlEl2tah5MECnA7nQ7y+TxkMhk+//xzHBwc4MaNG/D7/TyPjDAcDjmDSeXFR48ecTlzb2+P9ZhorYi/93XitQQyw+GQD41MJoNwOAy5XI5cLoderweXywWVSgWtVssGE9dSxWnMRqPBtWtqnSUm/LgtHtK3GAwGSKVSLPBnNpt501cqFda/iEajXEMslUpsq3E4hF8Fg8EA5XKZS0REQKTbUS6X49dRIJPJZHj6eqFQOHRTJJsf1bI9LiCNpnQ6jVqtBp1Ox+RTcSBTr9cRj8dZuIuUoichgBkFOcJms4l0Oo1+v49kMsm8D+LAyOVyHlVAAU25XOYbIZWVaAAgBTDjbqtRnRvK7AFAoVBgOkCj0eCuSLoMiPcIBTLEhWg2m6wgns1mUa/XD6msj14UxilrIebpjXJ8SMGesnXiUTzj8nzPC/Fn12630Wg0kMlkAICrClQRIZB9KCNOZUZqyInH41zepoD3Tfni10I0ETuAdDrNae69vT0YjUbMzMzAaDQy/4WY8ASxw75//z4SiQR2d3d5/AG1ab2uVq03DTo8B4MBp18/+eQT3L59GzabDTabjV/XarUQiURQr9eRSqX4tiNu+RyHZ35V0CLvdrtcTiuXyzAYDNjb24NOp+M1UK1WeUibQqFAq9XCwcEB3yDFJLVxBWUgiP9SLBa5NADgkPMVB/li0cBJA12ams0mDxWtVCrQarX4/e9/D6PRyEEJSTZQFoFKJNT9RWtk0uw16iNH24MpoKOL4lFdbOISJh1etMYo+DnKZuN2uNP7b7fbqNVqKBQK3Jzxne98h2fiCYKA/f19bmUHxu9ZnwWyBZVuu90uPvvsM2g0GqytrbEQIpVtAfD6oKYfyr4QDYTUtU/iMv7aGLPkZOiQKRaLHMlls1mYTCZ0Oh14vV6ukxHq9TrLpj948AAHBwfMtQGe1PfFE6LPOqjeSJmq7e1t7lTS6XT8mm63y3NhyGmMaxbhZUDrQNxxQbdBOrwLhQIPgKQbQ6vV4kOLNp9Y6XkSDqfBYMBk3Xw+/w3+Bh3C4sN40teNIAhc1qCWfLlcjv39fS6RULcjzTIrl8uHsrnUsTMJa+QoHNUNQl+jzJ1Y2XfUn4q/j2wkHuo6yoUZB398FMRNKzT+pVarQa/XY2ZmBnq9HmazGcVikbNTJHAq9jPj+vyjoDOLLkkk15DJZHgUyGhbPQW3xEekpovRNfOm99lrbf0ZTWmShDERqYrFIoxGIytsEnq9HorFItrtNiKRCMrlMhtx1NmM06IRv3fSaKDbDYHIVWK560l0rs8LWvhi8iFlX+jvYhIrOZLRtPokQByYkJMR/9to8DJJz34cRv0MkVrFnCjivZB2kPjgGWfi86tCfBmkdXNcIEN/F/8SB0rj5I9HQc9JF8j9/X188cUX8Hq9PBSZMg3A0dm7cX7+o3DUZYhKsqOUEOBJuZdsSCWkk75UvZEeZkqL03wSUm/d3Nx8oZ8xrix4McSTrXu9Htrt9jcWv/ggmvQb9bMw6iyHwyF3EIhfM4pJ5RLRejjv6+JpoOaB4/bVWRPOPCkcdci+zj0y7oe4OJDpdrs4ODhAv99HIBDgLrdSqcQlkjfJ8ThLGOU6iQewij/zozJ3gnA60ihv9H98mQ0zWmqYJDztmSb5mV8WFMwc5XglO0kQ46j1IO0pCc8LWiP1eh3ZbJYJ0t1uF7FYjCsLk0AIfxGIfbD4a0977WnijQUykiM5DMkeLw6y1fPcgiS7nk9I+0rCq4LWDimqA8Af//jHQ/8mzjSMeybqRTAuWe6zJ48rQcIIxmEjSZAgYfwg9i3EvSPCK4BDM/IkP3R2IZyn6FKCBAkSJEiQMFmYfOaSBAkSJEiQIGFiIQUyEiRIkCBBgoSxhRTISJAgQYIECRLGFlIgI0GCBAkSJEgYW0iBjAQJEiRIkCBhbCEFMhIkSJAgQYKEsYUUyEiQIEGCBAkSxhZSICNBggQJEiRIGFtIgYwECRIkSJAgYWwhBTISJEiQIEGChLGFFMhIkCBBggQJEsYWJxrICILwHwVB+FoQhLYgCP/1JP/vcYAgCDZBEP5eEIS6IAhhQRD+7Wm/p7MEaf0cD2n9HA9p/Twdkm2eDWl/HY/TtM9JT79OAPjPAP4EgPaE/+9xwH8B0AHgBnAVwP8WBOHecDh8dKrv6uxAWj/HQ1o/x0NaP0+HZJtnQ9pfx+PU7HMq068FQfjPAALD4fD/PPH//IxCEAQ9gCKAleFwuPXPX/tvAOLD4fA/neqbO2OQ1s83Ia2f54e0fp4OyTZHQ9pfx+O07SNxZM4OFgH0aBH8M+4BuHRK70fCeEFaPxIkvDlI++t4nKp9pEDm7MAAoDLytTIA4ym8FwnjB2n9SJDw5iDtr+NxqvaRApmzgxoA08jXTACqp/BeJIwfpPUjQcKbg7S/jsep2kcKZM4OtgAoBEFYEH3tCgCJSCbheSCtHwkS3hyk/XU8TtU+J91+rRAEQQNADkAuCIJGEIST7pw6kxgOh3UAfwfg/xIEQS8IwgcA/gLAfzvdd3Z2IK2fp0NaP8+GtH6eDsk2x0PaX8fjtO1z0hmZvwXQBPCfAPy7f/7z357wezjL+A943PqYAfDfAfx7qbXvEKT1czyk9XM8pPXzdEi2eTak/XU8Ts0+p9J+LUGCBAkSJEiQ8DogcWQkSJAgQYIECWMLKZCRIEGCBAkSJIwtpEBGggQJEiRIkDC2kAIZCRIkSJAgQcLYQgpkJEiQIEGCBAlji2N1AnQ63di1NDUaDeGk/i+DwTB29qnVaidmH71eP3b2qdfrJ2Yfs9k8dvYpl8snZh+j0Th29qlWq5L/OQYn5X8k2xyPSbOPlJGRIEGCBAkSJIwtJOVGCRLGFKQBNRgMjvx3QRAgCMI3viZBgoTXB9p/o5psgiBAJjsfuQJ69tPyL1IgI0HCGGM4HPIvghS8SJBwMqB9N7oHzyuGw+Gp+JszE8gMBoNDi0Emk52baFbCi4PWSr/fB/DksJbL5RN9cItvPjKZDHK5HBqN5tBeGQwGGAwG6Pf76HQ6h/YV3R6PytacZ/T7/UOHklwuP7f+h9YP/VmcWZDJZOd+3YgDFplMBpVKBZlMBoVCwXYaDofodrtoNpundri/KYj9ifi8FgSB/Q49L/mZN/38ZyKQGQ6HHMjQ7wqFQnK2ODpdeZ5Bm4WCmG63y18nhzvpNiLHKJfLoVQqYbFYoFQq+d97vR663S7a7Tb6/T47F/rewWAw8QHf80LsfMk24n8DcOhwOg8ge1BAQ2uN9pZMJjs3thjFUdlPrVYLpVIJjUYDlUrF/qler3MgI379JECceBAHK+LLJe0buVwO4M0++5kIZORyOXQ6HeRyOUe1rVYLrVbr3KXsnhW4HGWL0frkpGyWoyC+LavVauj1egBPbpG1Wg2dTueU3+WbAx0mJpMJPp8PJpMJCwsL0Gq1/Jpms4lWq4VarYZMJoNut4tWq4Ver4disYhGo4Fut4tut3uu6vhHgYI6s9kMuVzOv5rNJhqNxiHHfF5gMBig1Wo5UB4Oh7x+Wq0Wr5vzBjq0ZTIZtFotrFYrdDodZmZmYDAYYDabodFoOCiOxWK4c+cOms0m6vU6er3eaT/CK4P8r1qthkql4kuURqOBQqFAq9VCo9FAp9NBo9HgrDAFPoTXvX5ONZChIEWlUsHlckGn0/FiiEajiEaj6Pf7vAAmffPQhz2algOOrsWKywWj2YhJtBU9u1qthkajgdvtxqVLlyAIAmq1GlqtFtbX15HJZCbu+emzVigUUCgUCIVC+Pjjj+Hz+fCDH/wANpuNMwqVSgXlchnZbBabm5tot9solUpoNpu4ffs2kskkBzQymQxKpXLi7PUsUCZmMBhAq9VidnYWRqMRRqMRarUae3t72N/fR6fTOTf+h+B0OhEKhaDX6+FyudDpdBCJRFCr1RAOh9Fut89F5lMMytRRCcnpdOLKlSvweDz4+OOP4XK5+GLR6/XQ6/XwxRdfoN1uI5vNYn9/H81m8xsH+jhBfF5brVZYLBZcuXIFJpMJNpsNGo0G2WwW2WwWpVIJ0WgUjUYD6XSas8PD4fCNrJ0zk5Gx2+0wGo1wOp3Q6XSoVqtIpVIAMBGR7PNApVJBLpdDq9XyjUihePwR0SFFNyO6UdPGGg6H6PV6h9LC47phngbaSAqFAjqdDna7HdPT05DJZKhUKmg0GohGo8jn8xOdyROncEc/a+LMAI+dr8/nQ7vdhtFoRLPZRD6fh1KphEKh4O89L/tLDHKoSqUSBoMBfr8fFouF+Q7JZPLclLbp8qRWq6FUKuF2uzE9Pc3+uN1uYzAYcHBcqVTOhV3EoFKs2WyG2WxGIBBAKBSCy+WCy+WC3W6HyWSCwWDgy7fNZkMoFIJarUYulwPwOFtKGa1xsqE4E240GhEMBmGz2TA9PQ2z2QyTyQSVSgWNRsMJCblcjnq9DpVKhVarhUqlwufXqM95VVucekam3+9Dq9Xi5s2b8Pl8bBiVSoVEIoF6vc4lpkm7BYgPJLlcDqfTCYvFgqWlJSwtLXHphF7X7XYRjUZRKpWQTqeRTqdhNBrhcrnQ7XaRSCTQaDSQTCZRrVb5+yYFdGCbzWZMTU3h7bffxt/8zd9AoVAgl8uhUCggk8kgn8+j1Wqh3W4DmKybNAW05XIZ29vbKJfLcDgcfCNSKpWwWq2wWq3Q6/Xw+/0crHS7XSwvLyOXy+HevXu4f/8+stks9vb20Ov1Tr2F8qRANjQYDHA6nQgGg/iX//Jfwu12I5VKoVKpIJPJYG1t7VD2c1LtQuW1QCAAu92O73//+/j444+h1+tht9vRaDQ401kul1EqlfgwmmSbUJabSkkqlQrvvPMO3n77bXi9XqyurkKn08HhcECpVEIul3NZWxAETE9P41//63+NeDyOfr+PZDLJmRkqYY4DyO8aDAaYTCasrq7iL/7iL2C327G8vMzl/eFwiEajgUajgVarxVngRCKBarWKW7duIRaLIZlMIplM8j4kDtar4ExkZBQKBaxWK5xOJ1wuFywWCwwGAxQKBT/gJB3IhNGaocFggNVqhdfrxfT0NDQaDQwGAwckFMUaDAbmNZhMJni9XnQ6HbRaLSiVSuTz+YkLYsRQqVSHbCUmhms0Gr49TeLhQ5u/2+2iWq1Co9Egn8+j3+/DYDBArVZDp9NBEAQuQ5ENBoMBGo0GtFotcrkcMpkMBoMB4vE4BEFAt9ud2DUjhjizZzAYYLFY4PF44PF4uERJhNZJt4f4GXU6HSwWC1wuF/x+P3Q6HaxWKxqNBorFIgRBgNFohEqlYn80iXuMIAgCVCoVFAoFzGYzdDodvF4vpqam4PF44PV6oVKpoNVqIZPJuHRCvohe3+v1YDKZUCwWx5YoTb7VZDLB4XAgFArBbrfD4XBAo9FwpkXcrCOXy/kyWa1WEY/HmWdVqVS4IQF49YvCqQUylGGRy+XQ6/Vwu93weDzfIP2O6wf/PKBoVKVSQafT4ebNm1haWsKFCxewuLjIdqCbwWAwQDAYRLvdRq1WQ7VahVqthsFgQL1ex8OHD5HL5VCv11GtVg+l8CbB2VDw5nA4MDs7C4/HwyWSbDaLdDrNJSa6LU7CcwM4FIwMBgM0m00Ui0X0ej3cvn0bBoMBLpcLRqOReR/kMJRKJTweD9RqNex2OywWC4xGIy5duoSNjQ0ObDY2NtBoNCa+o4lS/0ajEZcvX+aDyWw2I5fLYXNzk2+R4oNpUkG+2Ov1YnFxEW63GzqdDgqFgveS1WqFIAjw+/0IBALIZDLMsZo029Bnrtfrsby8DKvVivfffx9TU1Pw+/3w+Xx8YaDzSdy+D4AvEhqNBmq1+htcx3GA+HySy+VYWlrCW2+9hcXFRSwsLEAmk2Fvbw+1Wg0bGxvIZDKoVqsol8scFGs0GtjtdqjVanzrW9+CQqHA/v4+9vf3EY1GmQxdq9XQ7/dfej2dakaGPmyVSgW9Xs91NgAc5EzaJiGIW9eIBR4IBHDx4kXMzMwgGAzy68SpN7vdzo642+1yZ0G5XEaz2YRer4der+cFMUk3JnoOnU4Hp9MJo9EImUyGbreLWq2GcrnMXRWT9Nxi0LrpdrvodDqoVqtIJpPMi+l0OnC5XGg2m/xvarUaJpOJs34qlQpmsxmhUAhyuRybm5tQKpXY2dk5tC4nDbQf6OBRqVTwer3weDxM8q3VahwQt1ot3l+TDPq86bZNWRcAzKPS6XTo9Xowm82wWq0olUoTmbESr321Wg2v1wufz4d33nkHS0tL0Gg00Gq1R0qGjO4Z4mCJs6Li/2ccQNwYlUoFt9uNixcvYmpqCk6nE81mE7lcDtlsFvfu3UM4HEaxWESpVILFYkEwGORgRqlUYnp6Gk6nE2azmb++vb0NAKjX669kk1MJZOiDp0xMIBCA1+uF3W5HuVxGo9FAqVRCvV5Hp9OZKKdKm5+ElPR6PS5evAiHw4H5+Xn4fD4YDAYAQLvd5hRcoVDg8gGRqjQaDQaDATqdDmq1GnZ2dhCNRlEoFJhXNEm3ScpMmUwmeDweWCwWAI/tFI/HEYvFUK1WvyGSN86gzU03PuoWmJqawnvvvQeDwcA3HqPRCI1GA5vNBrPZDACoVCoAgHQ6DZVKBY/HA5PJxCRxu92OK1euwOFwYH9/HyqVCrVaDe12e+Jas8UlJblczge3xWKBIAjo9/tot9ucJp+E9fM0kC3kcjmsViuMRiOmpqYwNzcHm832jdK0+NCedHI4HcIulwvvv/8+lx3VajXkcvkhTSYAR5YhyccTPcDtdqPZbCISiYxFFkusN0Vn88WLF7G8vAwAePDgAfL5PD755BNkMhns7OwwN5Far6n0HY1GodfrEYlE4PV6YbPZMDc3xw0t2WwWn376KYrF4kuToU8lkKEbkUajQSAQQDAYhNvtht1uRyaTQS6X44BGTJ466x/+84CenQIZs9mMy5cvw+fzYW5uDl6vF2q1mrUbCoUCKpUKdnZ20G63ubuCapN0O69Wq9je3sbBwQHy+TyazSZzJMYdYv0GuVwOo9H4jUAmkUggEolwSW2SsnnijjSDwYBQKISrV6/iL/7iL2A2m+F0Og8JcXW7XX69Xq9Hq9VCOp0GAGi1Wu76UqlUsNvtuHz5MqxWK27fvo3hcIh2u80lpkmCOJBRKBQwGo0cyBDHgZ590kqTR2EwGLCgos1mQzAYxOzsLKxWK4CjNaxGhdAmBeIgxGw249KlS/D7/Xj33XeZt0l+WayOTb+LhTrFAQ4FMi6XC61WC1qtdmzWFQVjHo8HoVAIi4uLuHjxIiKRCL744gtEo1H88pe/5Axmp9M5RAcR20mtViOdTiMQCOAHP/gBbty4AbfbDb/fj2g0ioODAwyHQ+Z6vqj/PtFTTtwiSt0VCwsLCIVCXG8sl8vMjqfe83H40J8F+lApk2K1WhEKheBwOLC0tASn08k35WKxyGJmGxsbqNfriMVi3JlkNBpx8eJFdshEvqN23EnLxBAomCEBJrlczjLglOIk8ti4Qxy8KRQKBAIBGAwGLCwsYGFhAXNzc5yBoaycuP2ebo1EFs9ms2i1Wnj06BGUSiWmpqYQCoXQ7/dhsVjQarVw4cIF5ls1Gg1+H8DkZLdIh0iv18NiscBut8NgMKBaraLT6SCXy/FFYBxuzi8LcTBCcg/UadLv99FqtZjcCTy+LBCXgQ6tSfAx4vXtcDiYf3f58mXuIiUyr/i1lJnq9/uo1+t8yVCpVN8o6Y8S78V8mbNoP3pOOltcLhemp6ehUqmQz+cRi8WwtrbGHX7tdvsbXcXi5yR75XI5DIdDPHz4EDqdjrvijEYjZmZmoFQq0Wq10Gw2X/g9n2ggQxGrTqeDTqfD7OwsPvroI7jdbthsNshkMqTTaWxtbXELMTnys/iBPw9GSwMmkwl+vx+Li4v43ve+x4GMuBMpGo3i4cOH2Nrawq9+9StWhhwOh9BqtVCr1fjzP/9zbodzOp18mJGK4iRCPFuI6vidTgeVSgW7u7vY3d1FrVYb27VCEB8yRAT/8MMPmQR+8eJFaDQaWCwWztwNBgO02+1D5G5BEOB0OqFUKrG5uYlsNouHDx8inU7jBz/4Ab7//e/DbDbD5/PBbDaj3W4jnU4jm80in89zZod+3rhDXNL2+/0IBoOYnp6GQqFAOp1GoVDgddRutycuIzWKwWAAmUzG2ijVahWRSARKpZL1ZFZXVwEAtVrtkOwDBXrjjNG5YwsLC3j33XexsLCAjz76iA9bcYaBMg50gWo0GohEIuj1elyWE88JpJ9NNhXPYzqLEPseaiQhkq9SqcTu7i5u376Nn/3sZyiXy9xwINY8I4hHewwGA+zv7+Pg4ADpdBp3797F9evX8W/+zb+B1+vF+++/j0wmg0Kh8A0dsOfxPadSdyDyodlshs1mg9Fo5Oi2XC4jn8+zPDgw3k5UTFBVKBRwu90IhUIIBALMV5DL5awN0mq1EIvFEA6HkUgk+GsU9dJNgESqJjVoOQqUiaFOABICJJtMWhBHPA4qpfn9fjidThgMBs5GdTodzkTR6AFynpS9Il2UfD6PXC7HmjvFYpEvCUqlkkXzDAYDdDrdoZ83SaBMg9FohFar5b2Xz+eZlzdJPKungQ7oVquFer2OZDLJGTqLxcLltcFgwN0oxIGYhLENYskGKrN6vV44HA7o9Xqo1epDTRPAk67Ber2OVCqFer2OaDSKwWAAr9fLZTkCvV58yTyrmRjgyXpXKpXc4UgcKvIj2WwW1WoVjUbjhfwtXear1SoymQz/ouqMTCaD1WqFwWBgrtrz2ulUAhmXy4ULFy5geXkZCwsLUCqVyOVyKJVKePDgAb7++msUi8WJkE6ntOLi4iLsdjveffddvPPOO7DZbJiZmeGUW71ex+3btxGLxVisjNqsxcqt4gGAFPnThpyEVO9RINIZbSziU8nl8kOBjFjUbRKg1+tx/fp1eDwefPvb38by8jIUCgWUSiWazSbS6TTy+Tx++9vfHiqJ0Bqg9dHpdJiIl06nUa/XsbGxwQTzYDAIQRD4NhkMBjmFTCWmcYf4UuTxeHDlyhXMzs7CYrGgWCziwYMHODg4QDKZnNi2YjHo+Xq9HnZ3dyGXy7G2tgaFQoFvfetb+OCDD/ggabVa2NzcRCwWQzqd5qznuNuHKA4zMzNwOBx4++238cEHH8BsNnMmRhx4CIKARqOBZrOJzc1N/PKXv0SpVEI8HodcLofD4YDL5eIuU/LVzWYTqVQKiUSCmzfEGmlnBeIOWavVio8++gg+nw8rKysIBAL45JNP8Mknn7B6eq/Xe67uYrE/AoBisciZHJVKhUAggL/8y7+EXq/H+vo62u02jygSd+wehxMLZMSOhOTlLRYLqwLW63VWjaS62zhvFqoZKhQKqNVq2Gw2+Hw++P1++P1+GAwGFhKqVCq8IcLhMOLxONLp9KHIXVyDpANKLPw2SjSbNNDNyWQyQafTQa1WA3hy46FnH/fnF+8ThUIBm80Gp9PJ+4UCNpqflMvlEI1GOeNSr9d5jdDa6ff7TGAlpexKpYJcLgeXy4V+v8/rVKPRcIst1bQnAeQQqQWdMsFE8i2VStztR/oZ4+p7nhfkO8QTmumwFpM2+/0+qtUqSqUS8xbHmUwv9s2UDXC5XKyQTZwYse8FwEFdoVBANpvl5oJcLge1Wo1Op/MNIjSJV9IQ5LPqp0f9Ds2y83q90Ov1rCmUyWRQKpXQ7Xb5gvm864DWG0mHlMtlJBIJpksYjUZuzc5msy/0/k8kkBGTh2QyGWZnZ/Gtb30LoVAIGo0GlUoFjx49QiQSQSwWQ7lcHmtnQoGFSqVCKBSC1WrFD37wAywtLcHv97OC6NraGjKZDH79618jk8lgd3cXuVyOuUHin0c2NJlMPKTLbDZDJpMxAY8iXZrBNI62G4WYID47O8sZBJ1Oh2aziUqlgmq1yvyQs5y2fRbE64YG9l28eBGhUAhms5kPmVKphO3tbfz6179GLpfDnTt3UK1W2bkc9fykB0KlokQiga+//hpKpRKNRgN6vZ7Jim+99RY8Hg+GwyH29vZOwRKvHzKZDC6XC2azGSsrK7hx4wYMBgPS6TRisRh2dnawv7+PRqMxtuvnZUCBCu0vp9OJmzdv4vr16zCZTFxGicViiEajHCiPK8TyF3Q5+O53v4tLly5hZmaGfepoyYTI9H/84x/x+9//HvF4HPfv34dGo8Hq6irPXKIWbQDcBUdlX5oGfRbtR+uApC2CwSCuX78Ov9+PbreL/f19Pp9rtdpLB7LiBoZWq4VoNAqFQoFIJIJWqwWn04nV1VX2cc+LEy0t0QNYrVYWx1EoFOj1ekgmk4jH4zyf4Sym3l4EpNFgs9ngdruxuLjIszloKCY50bt37yKZTCKTyaBWq3G2ZfTnAU/k+fV6PbRaLXcY0Ph0mrB6FjfLq4BsSe3nYoZ7q9X6xvDEcXx+sbaHRqPhYMblcnHbZrvdZqE70nKIRCK8Z55GJKRLAdmnUqkgFoshl8txAERDA2ny8ddff32Sj/9GIZPJYLFY4HQ64fV64ff7OdNQLBaRy+VQLBYnphvnRTAYDHiiM/H3/H4/l0c6nQ5nrLrd7mm/3VcGkXb1ej3MZjPm5uawuroKk8nEnKnRFmvKgobDYXz55ZcoFouIxWJwOByw2+2cZRc3plD2gfh7Yg7fWVtfYmkCk8nEz+Tz+XBwcMCcunK5zHvkZS/L9L29Xg+lUokv4NRNSAKVL4I3HsiIhXUCgQBsNhvfqmUyGVKpFGKxGDY2NnBwcIB6vT622QR6VrF2wHvvvcclJa1Wi0qlgng8fuhGHY1Gn6l/QgcQbT6j0chto1SSq1arryz1fJZA2RVKAdtsNng8Hm4prlar2Nvbw/7+Pisbj3MQRzV1rVaLmZkZbpEOBAI8PymTyeDBgwfY2NhAOBzmbjalUnnsZz769V6vh0ajwaRxcaqbBBfHXYOI/A51n1y/fh0LCwu4ePEijEYjEokE7ty5g2g0inQ6zcMQJ2HvPA9of5Fa7fT0NGeNdTodKpUKDg4OEA6HEY1GkUwmmTczzvYhcrvNZmONGFKVJ/9BF+xCoYB6vY6vvvoK0WiUfyfhNvF8IQDfWDtURqGGjbPqnygjo1ar4Xa74Xa7uTM0Go1iZ2cHsViMfSzw8sGYOJChi0QikeCyL5WXXuTnn4inotSl3+/H9PQ05ubmEAgEUCwWcXBwgEgkwo6Zyipn8cN+FiiQUSqVPFjr/fffRygUYmXIeDyO3d1d3L17F7/85S+59tzr9Y69UVPErNVqueNCr9ezND9xi+r1Oqcvx9GGR4Eksim7RcrHtVoNu7u7iEQiHMiMs4YOkZbpUJmZmUEoFILX6wXweA1QC/XW1hai0Si63S60Wu0Ly+j3ej00m02u69PPBx53LLzMzzxLEHMVaMDotWvXcPPmTdauoAMqmUwilUqhXC7zXJlJx1Et/hTI+Hw+6PV65HI5FtmMRCJIpVIc6I0zKJBxOBxwu908e0xcdlIqlRzI5HI5/OIXv8CtW7eQzWaZvyEWqqQsDzUg0L/3+310u10ekHhWKRP0DNR2TyNgKJC5e/cuk/+psvKyoOcXc6+SySQUCgUuXbrEYwxeBG80kBGXQzQaDVwuF4LBIPR6Pet/0FhvkkUfZ44DRZp6vR7BYBB+v59JhdSyGA6Hce/ePezs7KBer7OA23HBm5gjo1QqmeyqVCoxHA6ZJ0J1XHov4wyxU9FqtdDpdCzcBYD5MalUCtlslu077s8NPFlHo51oFCiPpr5f9JmfRTacBBuSrQThseCbwWDgkmyz2UQ4HOaafz6ff2Hi4iSASgkejwdWqxUejwculwsKhYJbZHd2dhCPx9FsNg91BY6bnWjNk4QDyeT7/f5vlDHa7Tbq9TrrU6XTaR6ISOq1VAah7AXxY8T7ikpydMmkbPlZtx29v06ng2aziVKphGw2e4gf9SrrQPy9pD9DWUHagy/6c99YICMuKVG3yYULF3Djxg3Y7XZUq1VO7cZiMU7hjWsbsbgM4nK5cP36dQQCAYRCIVgsFlZB/PLLL/EP//APqFQqKBQKbKNnRbji0pLJZIJer4dGo+HJz7lcjksFVGYYd1A3jdFoZHIzOZ1qtYpUKoX19XXmFj0rqzVOELfWi0G3vJfNPIlvi7RHCeO2544D2UkQBObGEJ9hc3MTW1tbuHfvHu7du8eH9CTIPTwvxKUEysIsLS1hYWGBB2dub2/jt7/9LfL5PPOHxlWclJ6XeGczMzP44IMPMD09DbfbfeiZWq0WUqkU0uk0fve73yGRSGB7exuZTAbA46YVi8WCmZkZBAIBLC8vM0dGnHFpNBqIxWKIxWJIpVLMRzvLGT/ynYPBALVaDZ1OB9FoFHt7eyyCKN5bYuG/FwFdUlUqFbRaLWsX0aTwMxPIEORyOde8LBYLd1+QUiSp+Y1zx4m4s0ahUPBmsdvtLNpGbXr5fJ6FpZ4nqhWnf0nRlsiuVHslbswkicEBT1oBSZTJYDAw6bXT6XA3gNiW4wxxuzTV1DudDjs/2uAvkzkQ22c4fCzTL1YuFd+0aOAbzTkbR5Cd1Go1S89rNBrIZDI0m03eh+12mwms4+h7XgbiQ0Sn08HhcMDpdEKv1zOJXuyryLeMs33ERHpquKDy/GhgQTIF5XIZ2WwWmUwGzWaTJ6arVCpYLBYedkyCceTryU40A48Cgm63OzYZLbrkUGmMStBU5qfyPtmVOEPP01ouzo6ZTCaOC0wmE/r9PldnXgRvNCMzHD4eR3Dt2jWO+KemppDJZLC5uYkHDx7g888/Z24H4ax/yKOgZ6XM0/T0NAsryeVyVKtVPHjwANvb29ja2kKhUDikCzMKsQZBv9+HUqnk1tHl5WVcvnwZJpMJmUwG8XgcGxsbyGQyhw68cYY4MDQajZzdWlhYgM/nQz6fRz6fR6lUYmd7nD3HBRS40TRvpVKJTCbDjpMUSOn30TTv0zCaeQEAn8+HhYUFzM/PHyL29vt9HuJGgybHCeLuC71eD6/Xiw8++AChUAgulwuDwQCpVAp37txBPB7ncuwk7JvnAdlHrVbDbDYjEAjgnXfewdTUFJxOJwAgHA7jiy++wNraGiKRCJf8x9lGlEVQqVRwu93weDyw2+2w2WxQKpUcqMlkMh47sL+/j6+//hqJRIIPcovFAo/Hg0uXLuEnP/kJHA4HlpeXuQGB9ppMJkO1WsXu7i5isdihstRZtqF4yjllXYjUTEFMKBTCzZs3Wfqj2Wzy6JPnGWtC3CKz2YyrV69ienoaV69ehcPhwKNHj5gu8CKJjTcSyIjT3kSqcrlcLAne7XZRKBS4pYuGbo0rxHwOcqBWqxV6vZ7b9gqFAtLpNMrlMmt5PK2cNHowUUuc1WqFzWZjOWeq41L9dVwzWk8DrR8a7key4YPBgNuuaeNMwrPTnhkMBqw7IR5PATxeC1qtFhqNhjlS4rZq8c8CvlnLJlkDUki2WCyHhBV7vR4qlQqrBI8rxC3sTqcTTqcTMpmM29dpMOso12gSMnvPAgUler0eRqPxGwc68WNKpRLvsXEvu4kvR+IxJ+K1L75AkrQD/aI5STRt3u12w+fzcaZYo9HwmB3giYZMuVxGpVJhUvBZpU2I35NYZBR4UuZWKpWHAmAAyGQy/G+E43wx/ZtcLodWq2UhQrPZDIPBwLyiVqv1Qu//tQcyFM1R7cvr9eKdd97B9PQ0PB4PlEolotEofvvb3yIcDvMk1Um5Ucvlck5pU7tsPp/HnTt3ePTCqNMcfWZaSBQUOZ1OfPe734XP58ONGzcwPT2N7e1t3L9/H/fv3+cJ2ZMghCdOAdOh7XK5uOtrMBggnU7j/v372NnZQaFQ4Llc4/7stC663S4HqORIu90u1Go1/H4/3nnnHej1etZdCofDaLVah5SfR2+H5LgvXLgAp9OJt956Czdu3IDD4YDBYEC/30cqlUKpVMLdu3fx8OFDxOPxM+t4nwZ6ZpoQPzMzg5WVFbjdbh6c+fXXX2N9fZ2dJZUWzkMQQzdts9mM1dVVbvF3u90stri1tYVbt26hUCgAwFhnYgjUUUQHMWm+iJ9NfCkgkvj8/Dwr/lIL/7Vr13jEDGVGqTxJF8xGo4FwOIw7d+4gl8uh3W6fef8k1nahqecqlQoOhwM+n4/pIRcvXsQ777yDcrmMzc1NJBIJ5PP5Q6MrRkvZwJNZS1arFRaLBVevXsWPf/xjOJ1O2Gw2CIKAVCqF7e1t5PP5F3rvbySQEXebGI1GhEIhTE9PsyR4sVjE7u4ustksyzZPwmahD1EsaEdcIBo/cBRJczSCFXdckHTz/Pw8QqEQ/H4/7HY7Hj16hIODA8TjcR4aOAk2JFBQSMMMxTXoSqWCRCJxiOA8bgfu00AZGRI5pNKHeHp6KBRCuVxGIBDgi4F4/hYFLxQQU2ZLq9XC7/cjFArh4sWLuHTpEtRqNVQqFXeBFQoFJBIJRCIRVCqVU7bGi0Nc5qUSAs3mKpfL2NvbQzweZ+LmWSZevimQjIPX62WOh8FgQLlcRrlcZqFOCvQmgTxP/oE6ZIgvdRSZnr6mUqngdDqh0+ng9XphNptx5coVvPfee/xzgCcXT/p5nU4HtVoNhUIBsVgMlUrlzEti0HujcSbiz16n0x0izNPkeJVKhVarxSK2RAY/6hyiM432ps1mg9frxdLSEsxmM7RaLfM9ya+/CN5IaUkQBDgcDly7dg3T09Pwer0wmUyIx+OoVCo8gOyouTDjDHEnCA3EslgsGA6HWFxcRL1e53KaOH1HhxT93Wq1wmQywefz4fLly3C5XFhdXYXVauVW67W1Ndy/fx/JZPJMpyxfFOKDiKSyfT4fPB4Pz8SJRCKcMZikKcW0F/r9Pq+Vra0tNJtNbj8ngtzMzAy+973voVQqYW5ujmco9Xo91hWimWZkS71ej6WlJbhcLlbv7XQ6SKfTyOVy+N3vfodUKoWdnR3k83m+RY4TKJNns9mwtLQEj8fDma2NjQ08ePCAg5hJWDPPC7oVk3SD3+9n+1DbMHHu6CAhrsMkQJwVeNqAWfI9BoMBFy9eRK1Wg91uR6fTgcVigVarRSgU4iyMuPQCPFlP6XQaGxsb2NvbQ61WOzS/66yCLo7tdhvJZBIymQxra2uw2+2YnZ2F1WplojNl7wRB4ArB3t4e8vk8CoUC8vk8ut0uD7DVarVQKBSw2+3QarVYXFzE/Pw8FhYWuLRNOk4kxyLuYH4evPZAhv5jm82Gt956i5ndBoMBt27dws7ODra3txGNRvn14+YsnwYKSiiQUavVvADm5+fR6XSwt7fHUtWkfSIOYobDIcxmM6fE/8W/+Bcwm80sGU7txhsbG1hbW2N+0VneJC+KwWAArVaLQCDAv9xuN9ecI5EIHjx4wOJ/kwTxrahUKmFnZweNRgOzs7Nwu91QKpXsUOx2O5rNJlZWVtBsNrnMtr+/j4ODA54yT4GPwWBg+W+1Wg21Ws0TsSORCH7/+99zu2ilUjlyVMZZBpF8yWmSgi+JJW5ubuLhw4dcMjlPIN+i1Wpht9sRCARw4cIF2Gw2aDQaDAYDZDIZHBwcsGYIMBnZGALZQMyrE2fIxYHMwsIChsMhLl26BAAslCgWvRPzboAn2dR0Oo179+5xIDMOYy8omUCBTL/fx8bGBjweD+bm5nDjxg3m69F8NgC4fv06Op0OgsEgisUi1tbWsLa2hkajwc0nNOj34sWLcLlcuHLlCl/MzWYz+6B0Oo1EIoFUKoVarfZC7/+1BTL0oSqVSp4o6vV6YbfbUavV0Gg0sLOzg7W1NZ6qe9Y/3OcFPUOv1+MP5f79+zCZTKxQ6Pf7oVQq4fF4OKihWw8NE6M24unpaYRCIUxNTbFWzP7+PjqdDu7fv8+pfxLUmwQbiiGTyWA0GlnZllokqSRA2TxKf07K84tveVReikQiHJzQZGqTycRtpEqlEl6vl2+N7XYbRqMRHo+HA2CtVguHwwG1Wg2ZTMYqo8PhEOl0Gru7u3wTJ5XpcdubRNqlFHggEIDD4YAgCIjH4ygWi8hms8zJG7fne1WIAxmbzcZqthqNhg/2YrHImkyTQJ4Xgy7MzWYT8Xgccrkc0WgUg8EAdrsdOp2OX0vBm5gzQ0HMUdxGUuwtlUpoNBo8NiWdTp/Z2UpHgQKxTqeDarWK7e1t5HI5tFotOByOQ2Kc1AHmdDpZDZgyLh6Ph0n15Mup28lisSAYDMJsNmMwGGB/fx+lUglffvklZwTpXDyVjIx4o1CP/crKCrRaLTKZDCqVCj777DN89dVX3C47KSCRINKkWFtbw//8n/8TLpcL77zzDmw2G65cucKkSgpeSGSKJlZT9E6BDLHBS6USvvjiC2SzWXzxxReIRqPMhqcPexw2yvOAbOl2u/H+++/D5/PB6XRCLpfj9u3buHPnDu7evYtMJjNR2TyCmKRbqVTw1VdfQa/Xw263o1KpYGlpCRcvXuQOCplMxiMMKBtIA+pG6/jD4WM9o3q9zoqj4XAYf/zjHzmgKZfLTFgfpzVFzxcMBnHjxg0sLy9jZmYGxWIRP//5zxGJRLC9vc1S6OOUaXodIPuYzWZMT08jGAzC6XRyVq5arSISiTCBfpL8M/AkkCmVSrhz5w4ymQyWlpaQz+fx1ltvsYaJmOtC3/e0nyeXy3nUR7PZ5Evmp59+is8++wztdntsMsbiji3iyPzmN7+BUqnEwsICBykajYbFSG02G370ox/B7XZzdg94wtMTd+dSlxJ16g6HQ27zj0aj+Id/+AfW66GS5ov49tdeWtJoNLBYLNDr9Xy402TZUqk0Nqm2F4X4AGo2m0ilUuh2uzw/itoXqTQwGAz4Rk2iQ3RbppsD3ZyLxSKi0SiLVFWr1UMtuZNiRyoNULZBvI4o0KPWPBqMOWmBjBgUlMhkMuRyOSSTSVY3ptZiGqhJtykqbxJBmEqX7XYbvV4P+Xye+Tf5fB7xeBzpdJqD6XFdU+RPtFotrFYrtFotZ0hpXg5lPCftkH4RkBYIZc5lMhk6nQ5arRZqtRr750kE7REaQZBOp6FQKBAKhZj0TvyXUd7LaBcOtVe3Wi0kEgnUajVEIhHu4KG2dfHPGCeQnShTR7xFjUaDWq3GX4vFYuh0OtDpdDwnTqvVHmpYodZz4PH6o0aGeDyOaDSKRCJxSHjxZao1ryWQEZOeAoEArly5Ap/Px6Sxr776CtlslqcU02snDWSHbDaLUqkEtVqNhw8fwmAw4ObNmwiFQgiFQggGg4dIvnq9nm8CnU4HiUQCDx48QD6fx/7+PgsrNRqNQwqKk3SrpIVO0729Xi+CwSCMRiOazSYajQZPSh/HbpoXwWgLY7fbxb179xAOh7G+vo5AIACr1YpgMMgHkrjGLwYFx8ViEc1mEwcHB6xplE6n0Wq1UC6XOZget0GRYn0duVwOl8uFhYUF6PV6xGIxRKNR3L9/H5FIBLVa7ZCQ4HkCrQ+FQsH8KPqsS6UScrkcUqkU4vE4qtXqRF40gScHdD6fx29/+1tYrVaUSiVMTU1hbm4Oc3NzUKvVLHB3lM4QZTbpEvDLX/4S6XQa+/v7fNGkoHkcbSh+z/1+H/F4nAnAxOHr9XpIpVJIJBLQ6XS4cOECa+v4fL5jx+5kMhlks1nE43Hcu3ePM8Rk65eJDV45kBEHMdQubLFYoFKp0Gg0UKlUkE6nkc1mmQsyyTdpyh40m03I5XJ0u13odDr4fL5Dw8YI4g3SbrfRbrd5GiiR72q1GrdYi1Oe47hJjgMFZ6NiVfV6nUsilHqctGc/CqOOkyYP09RYKj3SfKmnieJ1u13OSITDYeTzeWQyGWQyGQ5gKAU8rnYln0L6VYIgoFKpcEtxtVo98y2wbxrirJ34d7H4G3W+TTKGwyE6nQ5yuRw6nQ5zZmimG5GfxYEMcHg/EucqnU4jHA5zEEit1uMs8CrGcDhkLuKof6GhkiTtQOc7BcwUKNP3ke1SqRTPsspkMpz5eZXA77UEMpTSValU0Ov1HMSsra2hUCjg3r17yOfz3Akx6Y6EFH4FQeAU4+3bt7G5uYnbt29zLZEgDmRIkIiienLAFADS6ycNtMhpU/R6PcTjcQwGA+zs7DAjnrII48bheFnQ2qBArlarIR6PQ6PR4MGDB891KaDDilR7SZ+GnC3dnMbRnuR/SHOISrviGzMRfM+D73ka6Llp/djtdt5fyWSSBc2I1zGpAR+VjQaDAQqFAq8NvV6PL774gqd/a7VaAEeLJA6Hj+eRFQoF1Go17O7ucvMBlZMmBc/DQ6SxJqlUCgcHB5zNEvNhCGQ74uE0Go1XysQQXlsgQylLpVLJ/ejVahXZbBaJRIIVbSc1EzMKatHr9XrodrtIJBIQBIHHlR+FbrfLqU+xRDM56kl0LKOgWzVpxrRaLWxubnLqu1qtTlTH2/OCOFH1ep0zV3TjeZYd6OZN3QYkQEmlyUnYk9QWS7wiatPP5XI88I/8z3nmyJCAGWkNDQYDbhygYHdSsglPA60BajGv1+uQyWSIRCLMO3taWYTQ6XRYTb1Wq3G2dNKaL4BnDzUeDoe8lrLZ7KEBt0e9XjzH6XX5oFcOZOgN0A1ve3ub0+DUYtxqtSbqg30eiEmT4mcXEypHIU73jvJfJt1+5FxqtRoSiQRarRYqlQq63S6SySRPZB5XMuqrguwjfv7nbe0U62VQBudZXRnjAtpflJpeX19Hs9lEvV7ncrZ4KOR5DWLE+ysej/NkZgAoFApotVpIJpOHUvzjvjaehlEhO+BJ1nL0gKXXj5J+qStHTGKdlD31IqA9JQ7ingax7zou2Hmp93HcxtbpdC+868XOdnRDnMQH3Gg0TmwVGQyGF7LP0zomRmuDb9KJ1Gq1E7OPXq9/6VODAjoxTqI0UK/XT8w+ZrP5lewj/v1ZeF17sFwun5h9jEbjK/kfwklmnKrV6pn1PwSykdhOJ5VJOCn/8yK2Ee+lF8lGkb1e1/o6Sd/8smvnefA8Pull1tlx9nkjIwoIo4fzecdRrXzir0s2OhqSXZ4OyTbHQ7LPs3HebURZhRcpPUo+++XxJmz2RkYUSB/u0yHZ5/kwmt6VcBjSGjoa0v56NiQbfRNSYPL6cBo2HH+WnwQJEiRIkCDh3OJYjowECRIkSJAgQcJZhpSRkSBBggQJEiSMLaRARoIECRIkSJAwtpACGQkSJEiQIEHC2EIKZCRIkCBBggQJYwspkJEgQYIECRIkjC2kQEaCBAkSJEiQMLaQAhkJEiRIkCBBwthCCmQkSJAgQYIECWMLKZCRIEGCBAkSJIwtpEBGggQJEiRIkDC2kAIZCRIkSJAgQcLY4sQDGUEQbIIg/L0gCHVBEMKCIPzbk34PZxWSbY6HIAj/URCErwVBaAuC8F9P+/2cNUj2OR6SfZ4OyTbHQ7LPs3Ga55fipP4jEf4LgA4AN4CrAP63IAj3hsPho1N4L2cNkm2ORwLAfwbwJwC0p/xeziIk+xwPyT5Ph2Sb4yHZ59k4tfPrRDMygiDoAfwfAP7fw+GwNhwOPwXw/wPw/zjJ93EWIdnm2RgOh383HA7/AUD+tN/LWYRkn+Mh2efpkGxzPCT7HI/TPr9OurS0CKA3HA63RF+7B+DSCb+PswjJNhIkSJAgYRxxqufXSQcyBgCVka+VARhP+H2cRUi2kSBBggQJ44hTPb9OOpCpATCNfM0EoHrC7+MsQrKNBAkSJEgYR5zq+XXSgcwWAIUgCAuir10BIJFZJdtIkCBBgoTxxKmeXycayAyHwzqAvwPwfwmCoBcE4QMAfwHgv53k+ziLkGzzbAiCoBAEQQNADkAuCIJGEITT6Lw7k5Dsczwk+zwdkm2Oh2Sf43Ha59dpCOL9BzxuX8sA+O8A/r3UXsyQbHM8/hZAE8B/AvDv/vnPf3uq7+hsQbLP8ZDs83RItjkekn2ejVM7v4ThcHgS/48ECRIkSJAgQcJrhzSiQIIECRIkSJAwtpACGQkSJEiQIEHC2EIKZCRIkCBBggQJYwspkJEgQYIECRIkjC2kQEaCBAkSJEiQMLY4tg/eZDK98ZYmcdeUIAiv/PMqlcqr/5DnxEnY53XjJO2j0+nGzj6NRuPE7KPX69+IfZ7Vifgq+6xer5+YfTQazditn1arNfbr503ipNaPVqsdO9s0m80TWztGo3Hs7FOtVp9qnxMV9BkMBof+fpTDFQSBHe3rCGwknC8MBgMMh8NDa+g8r6PBYCDtJwkSJLwSBEE4dF7Tn0fP8NPytyeuTDj64OJDR4KEV8VwOOQ1Jt5853GNiZ0N2eI82kGCBAmvhtEg5mmBDOGk/cyJBDLkQNVqNWQyGZRKJRQKBex2OxYWFqBSqaDT6TAYDHDv3j2k02k0m000m81zdaOmbIL4F/B4UchkMilb9RSQrQRBgF6vh1KpRL/fR7/fx3A4ZLvS75OIo55LqVRCqVRiOByyLeh3wnleR7QmgCc3yfNsj2fhaetHLpfznycVYt8MHPbJkwq6/Iz6FplMBrlczme5IAi8Lnq9Hnq9HgaDAWeDZTIZ/7zRzM7rwolmZFQqFVQqFbRaLbRaLS5evIg/+ZM/gcFggMPhQLfbRa/XQ7fbRT6fR6PRADDZGwR4cgjRhz8YDNDv9/nfBUGAQqH4xuaZdLs8D0ZvBjqdDlqtFr1eD51OB/1+H51Oh+16XkAHjF6vx3A4RKvVOhTMnHeQg+73+7yvhsPhxB9OLwuxvcTrhw60SbcZ+Q+yg0wm4wP6PGA0gFOpVOxf5HI5n93NZhO9Xo+DGjq7xLZ6E8HMGwtkxA9OAczi4iLsdjv0ej30ej2mpqYQDAahVquh1+vRbDbZQORYzgPIEWi1WigUCqjVaqhUKnYO/X4fjUYD/X4f3W73UMADnO+ARhAEKJVKWCwWaLVaLC8vw+VyodFooNFooFAoIBwOo9VqoVKp8OaaZJCzsdvtuHjxIgaDAbLZLFqtFlKpFKrV6rnPPtC6MRqNAMD7ajTjcN5BmU46uG02G1QqFf97t9tFtVqd2ACZnt9gMECpVLIdut0u6vX6RK8Veja5XA5BEGA2m2EymaDX62Gz2aBWq2G326FQKNBoNNDtdlEsFlEqldBsNlEqlTi4oQCYsjSv2/e80YzMYDCAQqGA0WiE0WjExx9/jJWVFZhMJv7l9XoBAM1mE5VKBXq9HiqV6lxEu2InIZfL4XK5YDQa4fF44Ha7+QNvNBrY3d1FrVZDqVRCq9VCu90+dCif10NJEATodDpcunQJLpcLf/VXf4VLly4hn8+jUCjg0aNH+F//63+hWCyiXq+j0+lM7A2SbotUUpqZmcFf/dVfod/v49GjRygUCvjDH/6Acrk8sTZ4Eej1evh8PgwGA5RKJXQ6HdRqNXQ6HQDnd08RxGUFpVIJtVqNCxcuwOPx8IGeSqXw6NEjtNtttFqtQ+TycYc4++JyufiypNVqkclksL29zdmHSeWfESVEqVRibm4Oi4uLcLlcWFxchMlkwvT0NFQqFUqlEhqNBqLRKKLRKPL5PHZ3d1GtVrG/v88Xy263C4VCweVI+j9eNSB8oxkZMTdGr9fDYrHA4XDAaDTCZDJBp9NBrVZzencwGPCGmMTofhSUdjOZTNBoNAgEArDZbHC73XC73XzYNBoNDAYD1Go15PN51Ot1VKtVVKtV9Ho9tNvtib4ZHIXhcAi5XA61Wg2DwQCfzwev1wuHwwGz2Qzg8U3CZDKdi6BYDAqOFQoF9Ho9+v0+tFotNBrNIQdy3kB7RKPRQKPRwOl0YmZmBv1+H6lUih1tu92eyEPpZSGTyaDRaKDVauHz+RAKhThrDgC7u7sYDodot9un/E5fP8Qla6vVCovFAqvVCrlcjng8zufVJDYV0Pmt0+mg1+vhdrsRCoXgcDjg8Xj4TKdMlU6n48u1Xq/HYDDgM4qy461WC51Ohy8L9P+8Kt5oINPv9zmadTqdmJqawvT0NAwGAwwGw6HadKPRQLVaRSaTQTweR7fbndgDSJyJMZlMePfdd+F0OvH2229zMGOz2fg17XYb8XgcjUYDiUQC1WoVe3t72N/fRzqdxtbWFt8MzgOoBKDT6eDxeDA1NYUf//jHCIVC8Pv90Ol00Gg0cDgciEaj6Ha76HQ6E3trGgXxY+gWLQ5kJnVPPQ+I4+ByuTA3N4fl5WX85Cc/Qbvdxq1bt5DNZvGLX/wC5XIZwGQdSi8K2iuCIECj0fAl4cc//jHeeustvqh+/vnn2Nvb4wsW+e1JsV2/34dCocDU1BQWFhawvLyMpaUl3L59G8ViEYVCAZFIBN1ul0swkwK6LM7OziIQCODDDz/Et7/9bU5MUCWBAhcACAQCXE4qlUqo1+vY29tDtVrF5uYmcrkcNjc3sb29fYgLSmvmZc+wNxLIiKNTKi1ZLBYYDAbOwsjlcu4aEJOojmKHTyLISSiVSs7C+P1+hEIhmM1mmM1mfk2n04EgCGg2m1AqlahUKuh0Omi1Wuj1elCr1QDAbPFJtdkoxGvL6XTC7XZDp9PxbZE2Gtll0gO9UUIePT+Ac0dOHIXYNtRc4PV6MTU1hWaziWg0in6/D5VKdW4C3uNAa0ipVEKr1cJqtcLhcMDtdsPr9XJThslk4lLBpNqMqgoGgwF2ux0+nw/RaBQGgwGNRuONdeKcFYirKW63my9J4i42MQmYsjN6vZ6rCdVqFY1GA2q1GrlcDrFYDN1u91A261Vs+NoDGQpMlEolp/w/+ugjBINBBINBaDQalEol1Go13igymQxqtRo2mw0rKyvodDo4ODjA/v7+oZ89CRtFXHJTKpXQ6/WYnp5GMBiEx+PhtCWVi4gkRWWCmZkZDIdDBINBXLlyBWtra+j1eigWi9jb20Oz2QQwGbY6CmICms1mw5UrVxAMBuFyuWAymbgdsFAoIJ/PI5FIoNFoTHT6d/TvKpUKBoOBS7jtdhudTgfNZvNcdW4RyEZ0gZqZmcG7776L2dlZOBwOVKtVdsCTtDZeFKOBMHEhXC4XPvzwQzidToRCIfT7fcRiMcRiMWxsbCCfz6NWq03sJYr8MGWbtFotBzWUiTmqTXkSQBk5vV4PjUbDRO9erwcAfD4VCgU0m03I5XIoFApoNBqYTCYYjUbMzs6i1+vB6/WiVqvB7/fD7XYjHo/j7t27TLR/Fbz2QIayKvSBm81mzM3NYXp6GhaLBQqFAu12G9lsFgqFAlqtFiqVCna7HQDg8XgQCoVQLBb5503i5gDAka3FYoHdbofBYIBWq0W/3+csQrfbZcIZtbtRFicQCKDf7+Pu3buQyWQIh8MTbS+xo6Wbtc/ng8/n40wfPXuz2UQ+n0e5XEa73WY7TqptgCdBslwuh0qlglqthkaj4VbI1+EwxhVUbiNfMzU1xXX+0XLIJB5Izwtxa7HBYMDU1BT8fj+uXbsGp9MJrVaL4XCIUqmEaDSKTCaDZrPJF4VJ7YajCzpdvtVqNZewJzHTKd4DCoUCKpWK9d/E3X10VpXLZZTLZX6N2Wzm816n0wEAbDYb82UqlQqGwyHW1tb4Z52JjIy4Y0Kr1cLr9eLq1asIBoOYmZmB0+lEJpNBOBzGvXv3cO/ePej1ejidTjgcDnz44YcwGAxYWlqC1WpFv99nLZlCoTAxGg/0/geDAVqtFmq1GqLRKADA5/PBZrMBeLx46vU60uk0Go0GIpEIer0elpeX4Xa7oVQqodFoYLPZMD8/D41Gg/X1ddbemUTQQnc4HLDb7VhaWsK1a9fgcDh4s9Dr9vb28Omnn2Jra4tJZpN6YxyFwWCA1+vl7rdKpQKFQnEuD2i6WMnlcni9XlitVszMzCAUCsFisfA+LBQKyOVyE0lYfVG43W54PB7Mz8/j5s2bsNvtsFgsfGgPBgOk02ncv38f+/v7fFmY9CCZziDKOBiNRhgMBi6rTZJ6Nj2DQqGA1WqF1+tlAi/5kVarxZ//7du3kUgkOODxeDy4dOkSdzZptVqmmgSDQQiCAKPRiFQqdaia8LJn/GsPZEilNxgM4lvf+ha8Xi9mZmag1Wqxvb2NcDiM3/zmN/jkk09gs9kwPT2NmZkZ3LhxA1arFRcvXsTc3BxyuRz29vaQyWSQz+c5Gp4EUIcWaTBQff7KlSvo9/ucqel2u8hms8hms/j888/RarWg0+n4VqnRaGC32zE/P8+Rb6lUOlS3nCTQGrNarVhYWMDS0hKuXLnCmSxyJIPBAPv7+/jd737HG6Xf7x/K2EwiyD4GgwEejwcejwcul4s5DOeBJ3QUSAaCiOEzMzMIBoNc5+90OlyKbLVap/12TxWCIMDlcmFlZQWLi4u4ceMGDAYD367pNp5MJnH//n1kMhmUy2UMBoND2leTCqIEqNVqGI1GFoSbxOemoM1ms8Hr9cJgMBzSLmu1Wtje3kYymcQnn3yC7e1tts3MzAwajQY8Hg9rzZBGXCAQgMfjgVqtRjgcRiKRQCwWQ71ef+kz/rUEMuKUv9FohMvlYk6MyWTijMr29ja2t7eRSqXQ6XTQaDRQKpWQzWaxtraGUqnEhE2Px4PV1VXs7+8jmUxyxD8JB7S4ddhoNMLtdnN5RC6Xo1KpcIBz69YtFItF7O/vo9Pp4Ouvv0YqlcLFixdx8eJFNBoNVrOdlEDvKIySNV0uF6xWK9dtKTgsl8ucxatWq0yUnmTbEMS3KJVKBYXi8fY+b7IGTwOpipNDJcXjer2OXC6HfD6Pbrd72m/zxEF7iwJel8uF+fl5+P1+GAwGzsQMh0PWYyoWi6hUKmi1WhNbTjoKdFEi2QviMk7SBYHOJ51OB5PJBKvVCqvVypfFdruNarWKdDqNjY0NxONx5HI5VvXtdrtIpVJYX19HNpuF0WhkKoTJZGKBXJPJhEAgAACHiMEvg1cOZEYJlB6PBysrK7h69Spu3LiBwWCA9fV15HI5fPLJJ3jw4AGKxSIajQYHJY1GAz/72c/gcrnwk5/8BBcuXMDS0hI8Hg+++OILrK+vo1KpoF6vsyMe141DG0Gj0cBiscDr9eLSpUsIhUKwWq1QKBSIx+N48OAB1tfX8fOf/xy1Wo3JdJubm9BoNPjLv/xLJl5ZLBa+MVHqd5JEmsROQhAEOBwOLC4uIhAIwGg0cqdJt9tFJBJBOp3GwcEBstnsoXTlJNjiWSAlbaPRCI1GA+AxIa9arbKy8XmDWAvEbDZDp9NBqVSi3W6jUqkgl8shEolgf38f9Xr9XKwTMajkSvo6i4uL+PDDD2E2m2G327kLsNvtIhqNIpVKYX9/H9lsFu12eyJK/s8LyqS3223U63VWrSWMux3EPDsq4QcCAYRCIRgMBgBArVZDOBzG/v4+/vEf/xHRaBTNZhPdbpf9bLFYxMHBAcxmM6LRKJxOJ7773e9ibm6OGzPcbjeuX78Oq9WKzz777JBo3ov661cKZMSHpUKh4Hqa3++HxWIB8LiOFo/HkUwmkcvlWF0VeLwoKDOTTqc5krNarQAeH9A2mw1OpxMKhWIibpTkVKk8RPVnk8kEAGi32ygWi0gkEshkMqhUKmg2mxz5099rtdohkuK4b6DjQM9Hqs82m42F7yjTQjekXC6HeDyOYrH4DfXjSQc9J2X6iGdVrVY5GB73/fOyECto08Hc6/VQqVT4kkT+5bysFwKRe/V6/SEZejGRlVplC4UCkskkKyGfJ3VxMRmcSmzicu2kZGUokDGbzbDZbNDr9VyW7/V6qNfrSCaTSCaTqNVqPD6HvlcspSKXy5FKpdDtdpFMJnk8EfGNqJpAWRoxafxF8MoZGRK9o8P4+vXr+NGPfgS5XI5MJoNoNIr/8T/+Bw4ODjiQIS4NAI5qS6USNBoNFAoF1tfX8e677+Ltt9/GpUuX8MMf/hDxeBw///nPkcvl2GDjuHGI6W2z2fDee+8hEAhgaWkJdrsd5XIZ0WgUd+/exa9+9Svk83nmvNCzkv1qtRrryohnn0wiSJ/g0qVL8Hq9+OCDD/Duu++yKFO320WpVEK5XMZnn32G27dvMwkNwMQHegQ6kIhop9frsbm5iWg0iq2tLUQiEa5Dnwd7EOjzV6vV0Gq1LLleq9Xw6NEjLl9Taem8rBfxJVSj0WBpaQmhUAiLi4twOBxcmux2uygUCqhUKvjiiy+Y5Fur1QCcn/0FPOle6vf7h7ohJymIIT7h6uoqgsEgfD4fjEYjWq0WyuUytra28Pd///fMX6UW9NHy/XA4RL1ex/3796FWq9Hr9bC+vo4f/vCH8Pl8EAQBJpOJExakNfMy85heC0eGZIxNJhNnUJrNJgqFAkfwqVQK7Xb7G9Nmqd5IWh+pVApqtRq1Wo0JrC6Xiw9t8cONYzBDz04S6Q6HgzMNrVYLpVIJ+Xwe2WwWtVqNFXtJ2Iz69sWj0oHJuQ2IIW7npGDZ7XbD4XDAYrFwAEdOpdFoIJ/PI51OszS2WH1yEm0kBj2rRqNhQcVKpYJyuYxarXaoNHueQGtITDgUBAHdbpczMiSdPo4+5VVB2h8kLEnlWjqY+v0+6vU6KpUK+yZaS9TWfp4gDlwm0adQtoSCDEowUIdfuVxGKpVCPp/nPQMczlgRaOBxq9VCOp2GQqFAsVjk7CfJRFBm5mXX0ksHMvRmqQ14dXUVs7OzmJubg0ajQSKRwO9//3vEYrFD3QCj47wJFNhEo1FUq1XMzs5icXERzWYToVAIg8EABoMBarUa3W537FLAtPhp6Nj09DSuXbvGNehGo4Gvv/4aW1tb3A1A2S7g6YO1yEmPky2eF+Ibo1arxeLiIi5fvoypqalDCr69Xg/JZBLpdBrRaBTxeBytVutQN8EkOhwC2YnKAYFAAPPz80in03j48CGi0SiXJIHzc4Omz5y0PxwOB/x+P4xGIwaDARqNBuLxONLpNKtkn4dSLfCkPKJQKFhyfnZ2Fqurq/B4PIdaihuNBrfXbm1tIZFIvFKr7LhDXKactOcXc2RINV2r1UKhUKBUKmF/fx8HBwdIJpOoVCrf6CYe5TNSNl0QBGQyGTQaDTx8+BB+vx92ux3T09PMEzUajSiVSqhUKidbWhKLTJHugHis997eHg9j6/V6R6af6A3T12m6czabRbFYhEwmY3Ed6sQYR8KiuD1dr9czl4iGGrZaLcRiMWxubiKRSLDy8fNEqFRSmKTOHPGth9aY0+lEMBiE1WrlzUGp0HK5jGKxyMJM56VTCXgSyJD0gclkgt1uR7FYRKlU4hsQTZ49L3Yh0FogqXW1Ws1t19QhSGKB5/FwpmDGZrPB4/HAaDQeukB1u13E43FEIhHkcjmUy+WJFr97FsRZ4kkMZghE/lYqlZDL5Wi1Wkx3qFQq3LBz3JwkccauVqtx1SUWi3FJ02AwwO12o9vtMl0AeLGKy0sFMnTIKBQKJqteuHABq6urAID9/X3s7u5ie3sbhULhmXVncVaBCJoHBwe4desWXC4XFhYWWCbZYDDwEMBxcjpkM4vFwmqZZrMZSqUS6XQapVKJxzLQQTwKKuGR6iaptpIzFo+Up9ePO7RaLZaWlnhSsc/ng8FgOKRWWyqVsL6+jkgkgmKxeO4OJHpeh8MBl8sFm83G87eq1SqXASY5K3Uc6NBRKBRQKpUYDAZoNpuoVqvsmM/T0FXCqFK2eEYZETtbrRaKxSKi0SjC4TCq1Spnw8/L/iKQ4CtlsKxWK+r1OhQKBdtjkkqTYuIu8LhM1Ol0XpgXRPagSkoqlcKjR48gl8tx/fp1Ps9MJhOPEXnRztuXzsgQI9lqtcLtdmNxcRErKyvY2trC9vY29vb2sL29ze1Uz7oFUmmJWMvhcBg6nY71UqgTw2AwsLzxODke+mBMJhNmZ2fh8/lgNpvR7XaRTqeRTqcRDocRDoePDPzoAyWyol6vh1arZbln4tNMgrqmeAMRCTEQCGBmZgZer5cXOk1ZLRaLWF9fx/7+Pgcy58XRUolgOBzCbrcjGAzCZrMdItO/ij7DJEAcyKhUqkOq2rlcjgn15xFiH0olOApo+v0+arUajyOIRCLc+TbJmYijIC65EKfDarWyavZoeWUSbDN6xooJzi8irkkBHl0WkskkVCoVrFYrd73RbDjxQOkXwStlZIiASY5TEAQm+ZJmxYs4CPGHT6SiTqfD2RhiNudyubEJYsRiU0SgstvtMJlMPJm51+uh0+lwO6NYSl/8nHK5nGuLHo8HOp0O5XIZ+/v7SCQSLMMPjG82RlyCMxqN8Hq9PEaebEYbo1qtIhKJIBKJIJlMIpvNjl2m7mUhvk2T0JvL5UIoFIJCoUAul0M6nUYikUA2mz3EtyJMso3EHBAanmk0GqHT6dDr9ZDP51EoFFAsFlGtVtmfnRfQ+lGr1fD5fHA6nbDb7Uz0JQ5RIpFAKpU6pOM1yevmeSCmQ5ynNQO8ms+gPalWq2G1Wnm8w+u4RLwS2VepVCIQCCAQCLBYTrlcxsHBATKZDNflX4RAR6+r1WpIJBJYWFiA0WgEAExPT0MQBMTjcWSz2bEQfaP3R7VGt9uNmZkZeDweqFQq1mdoNBqctgO+yQAXhMfS2FeuXMHy8jIuXboEq9WKnZ0d/OEPf0Aul0OlUuED6yzb5DhQayPNkJqZmcG3vvUtBIPBQzLpg8EAmUwGn332GaLRKB4+fIhsNnuuykqUFdXr9TCZTFheXsb169ehUqmws7ODzc1NPHjwAJVKBZ1Oh2/a58E25DRlMhmcTiecTidcLhccDgdyuRxisRj29/cRi8W4XHJesgxin6LX63HlyhX4/X7Mzs7C7XYzp6FYLOL+/fuIRCI86kPcQXlecVQZ7rzhZZ6Z/LbZbEYoFILT6YRSqTzdQAZ48oGKpdA7nQ5arRa3Wj9rRtJRmRVxtwERqsTTfMdx8YifgwSAKLPQ7Xa5fnhUYCaXy6HVamE0GuF0OuF2u6HVatHpdNBsNrm0NO7pcXKSCoUCJpMJfr8fPp8PJpOJu5Sodk9EzXQ6jWw2y10nk15SErd9itskrVYrLBYLrFYrGo0GyuUyE/JeVmRq3EH2ofo77btOp8MS+8TJA85HN5e4e9JsNsPlcvGAUdpj5JNodEOxWESn0zlUnpx0O41CfLEkbh4JlU7yDDPxnhD/eZQ/cxxo3SiVSshkMhiNRs7+0c+jIOdl7fjKOjL0cCTbXC6Xkc1mWfmRMgRiFjzwTYdMf6bDXKVSscieXq8H8Fjpt1KpMJFR/LPO6sYabVOn0hJNEu10OqhWqyiXy0d2YxEvhgivN2/exOrqKur1OhKJBA/cokBm3II8cecRcYiMRiNWV1fx13/913A4HAgEAsxmJ22UTCaD9fV1/P73v0exWOQRDpN8WxRvcso4aDQaLC8vw+fz4fLly1haWsLt27exubmJcDjMCsfnJdtAIL+iVqsxNzeHQCAAn88Hq9WKO3fu4M6dO9jZ2UGtVuNs1XmwD+21YDCIP/uzP4Pb7cY777wDm80Gm80GhULBStDhcBi3bt06pGl1VOfpeQGJb9brdRQKBcRisUNzACet1HTU89AeIdrI6J45KtAhzqfX64XRaMSlS5dw8+ZNOJ1O5jtSAuRleZ6vRdmXMi9ifgZlIEbb9J7WoiX+Pkp5Uk1bnMIbDYjGBZRpUKlUXGYiwmqr1eIPEfimEJxarYbdbueOFIvFwmMKSFqdRhiMG0Y7rEjMzW6388FDImb0+mazye3WxMcanXcyjrZ4UVC5kUY2UOaq1+uhVCrx4XOeyibA4f0j5sio1WooFArOyFDwfx7Wyih0Oh18Ph88Hg+cTicP8wOATqeDWq2GSqXCkgbjKHnxJkA+mygBFMRMIuiyRBB3/imVyiOrCEftJYoFKBNDQbNer0e/3+fsFg2dfJlGnpcOZCjFn8/noVKp0Ov1oFKpEAqFcPPmTWZ0E+udWqYpa0AHO81woL8HAgGYzWZcvXoVq6ur8Pv9EASBe9hpUNmo8M5ZhJjpbrVaWafB7XZDpVKhVqshn89ja2sL4XCYBYbo+wwGAxwOB3w+H374wx+y0JnZbMb6+joePnyIcDjMA7uAs2uLp0FMOlQqlVhZWcH169dx4cKFQxPBacH3+33s7u7ij3/8Iw8jpfVwHhR8xdIHarUaLpcLN27cwNTUFEwmEzqdDiKRCL788ktkMhkAOHdBDJWUdDodLBYLpqenMTU1BZVKhWaziVwuh3A4jHw+D2D89syrgC6FBoOBSb40S2c4HKLdbmN3dxf379/H+vo6otEod72dp3X0NNDhLm7QmCR/QxeAwWDAF0XSgTObzZiZmUGtVsPly5dRKBS4M5kydXR+ibsEnU4nDAYD3nvvPYRCIVy7dg2BQADdbhfZbBbxeBxffvklotEoMpnMyQ6NpIet1+t8sxEEARaLBaFQCM1mEy6XC2q1modK0SIQs77ptk0zg/x+PzweDy5evIjV1VXWDOl2u6jVaixeNW6gmrTBYGBiNA3MzOVyyGQyaDabh9ppFQoF17EXFxcRCoW4Q4yEhUinh27d4wZx6U2tVsPj8WBpaQnBYJB1doDDN6FMJoO9vT0kk0lu76f66yQ5laeBHIVarYbBYIDf70coFIJGo+FsTDQaRa1WO5TFPE8g36LRaPgSQV2CxCFqNBoAzk8gQ+uG2qxJzoKERqlrMp/PY29vD/F4nDtHgfPBIXoeUHmOyiDjJgXyPCApFGpCoTK2zWaDy+WC3++HXC7H3t4el9XIDuIym1qthsPhgNVqxfz8PObn5zlZUS6XkclkvqFT9DINGy8VyNCC7vV6PLU6EonA5XJBqVRicXERZrMZRqORW2QbjQbzZig1Rb34lK5SqVSYnZ2FzWaDz+eD3W5Hs9nE7u4uYrEY9vb2EIvF0Gw2D5Wjzjqoa4l65UnUjxwpvYZgMpn4gLp27Rr8fj9cLhf0ej3S6TSazSbW19exvr6OTCYztpuIFr1cLofX64XT6cTy8jJWV1dhMpkOBbwkpFQsFrG1tYW1tTUUCgUAOBeZGII48DObzbBardyNQ7X7bDbLRFZgPPbIm4Q4M0q3RK1Wi3q9fm5sI9axslgs7E+0Wi3fpMvlMur1OsLhMNbX17ltn3BebHUcSO6AVKIbjQYL4k0CqDrSbrextbWFarWKlZUV7maz2+2Yn5/Hd7/7XWSzWWg0GhQKBZTLZbRarUOT0y0WCwwGA1cRLl68CKfTCbPZzGrsOzs73BVHAyhfhuf50hkZOlzoYI1Go/B4PJiZmcHMzAyCwSDm5+fRbDZxcHDADPh6vQ61Ws06IR6Ph9PkCoWCJ20SYrEYdnd3EYlEsLu7yyMPxoXUSgcPBTI0xpyi3aNgMBh45MPNmzfhcrngdDqh1Wqxu7uLZDKJjY0NbGxssHbMuN6WSOvD5/NhZmYGS0tLWFlZ4e4AADxPKZ1OIxKJYHNzE2tra/zc45iJehVQtk58KNntdmxubnIXF5UpAekAIlDml7ofKdt3HkCBjMFg4EuD0WjkQGY4HPJQyHA4jI2NDe48PW8aO08D7TsKhC0WC2q12qG5VGe58eR5QBncTqeDnZ2dQ0N4yd8YDAa4XC7kcjnI5XJks1ns7e2hVCrB5/PxGe52u2E0GllCxWQysRq9OJCJRqMcyLwsYfqlMzJ0yyGy6s7ODg8/FE8d7vf7rNinUCjQ7Xah0Wg4mDEYDIdmBZVKJVSrVZ5KG4/Hce/ePTameELtOC2YXq+HdrvNddV+vw+lUgmtVguPx8N/r1armJqaQigUQiAQQDAYhF6vR6lUQqFQwMbGBg4ODhCNRlmnZ5zsQKDPkG6FpGNhsVgOtd8PBgOW2Se1aBqoeZ6h0+ng9/vh9XqhUqkwHA5RKpWQTqe5g+s8ZKgkHA+xIKcgCLBarZiamuJ2a6VSyaKc6XQayWSSpxqPK+/uTUJc4m40Gmg2m3xhmJSAj873RqMBpVKJRCKBg4MDBAIB6HQ6AI85jf9/9t7rR648ve/+ngqncs6hc2Azh+HMTp7ZVVgJkixBsnUh+AXeWxv+A3yh98bwv2D41oAN6MKAJK+llWZntTPL2Z3AHLrZ7FxdOecc3wvq+fFUkWxyhp3q1O8DNJpsNrvrPPULT36oSz0l8FarVZbIS9EWKt6QduytVquoVqvMSZFMJln48ofyRjkyg8GAxVVv3LiBe/fu4a233kI8HofD4cD8/DxEUYTT6RxqxqXX66HX61l2PC2ObreLSCSCUqmEjY0NbGxsIJlMYn19Ha1Wi+XajFPcn+REHpharYZarcbihxaLBefPn4fP52OyPHv2LM6cOQODwQCHw4F6vc5cvf/8z//MmpxVKhWm0I3TYUMhJVEUWRXWlStXcO3aNQSDwSFPC41wyGaz+O1vf4s7d+4gnU6znjGT5o0h7HY7rl27hqmpKej1evT7fUSjURZuJEVvXPbJUTKq0I3bfnkTyGAQRRFqtRrz8/P40Y9+hJmZGdhsNiiVSnYmbWxsYHNzE+FwGLVa7bmKwklHEATWpp/CuFTRJSejgZ6Ths0+evQI7XZ7qGRap9MxrxSVT5MxrlKpmPdTOviYKpxpHM+9e/dw48YNVCoV1Ov1obvs2KqWpJD2Ri8yFAqxS5Y0N5VKxdr06/V6GI1GNoaAKlIo16ZYLGJ/f5+5m2q1GisjHdfNRbNdKGGZ5iWJogiHw8EqvwaDAYsjUqyyWq0iFouxC71arQ5Vbo2bLIBnpcNOpxNOp5MpNFqtli1k8vZRuIRKraXJh5MKKcJKpRKNRgO9Xg/5fJ6FbznPkB6OUotarmWzo0jzg6ixpsFggFqtZmd3uVxmRQdkXAKTvccOQtonRY7JvsCz5n/5fB7JZJJ9kAJDCfUA2FqiyAqFj6hMvdfrsQhCNBpl4SRqH/KmIbk37uxLn2k43a1bt/Do0SPodDo4nU42Hp5iiwqFAhaLBWazmeXNUNiFHrJUKqHVarEY7bhmzZPnaDAYsNDYxsYGZmZm4PF4cObMGVitVly/fn0oVKLVaiGKIvL5PDY3NxGNRvG3f/u3SCQSTD4AxtIbQQtWqVTCYrHggw8+wPT0NC5fvoyFhQXmuWu326zp3+eff45IJMK8DbwUFOxSAoCtrS10u13cuXMH9+/fZ7PIJlk+BK01ambWbDZRqVRQKBSYwjdu58rrIq0goXl1brcbMzMzcLlc0Ov1qNVqbFbZrVu38ODBA1Y9KUeZ/FBGe5lR1a7cleFut4vV1VXs7e0hnU5jf38f09PTeOedd4aKdcjzS/Ko1+sszE2NJzOZzFAjV5pz9jq9aF7FoXT2BcAu4nK5jFKpBI1GwxJ7S6USc21SS3WTycTcc1JFJpFIoFKpDP38cW2CJ4U8KMViEdlsFjqdjpVMU4k5HaikydKbn06nkUgkkEgkmHdqnLtISpuV6fV66HQ6Fqakw7fVarHkw3g8jkQigWq1+oN6DMgRcufW63XWWymXy6FYLKLZbJ70yzsVjPb8aLVaaDQarJFZp9ORpSX9IqRNSqWtCjqdDmsuWSgUUCwWJ0Ym3wdSWvr9PoseSBvCyRHyYlarVdbuw2w2Q61WI5vNwmQyMa+MtDv/YDBAqVRCNptFqVRi88zImE+n00in0yyKI23J8kM5lNASMDwRFHj6xlPn2UqlMjQ4ktycdGHTgUOHs/SCHqcy6xdBz0wzXdbW1tBsNnHu3DkYjUbodLqhcMpgMGATnWOxGB49eoRisYhoNMqmz46zN0KqdVcqFXz99dcwmUy4e/cuTCYT2xBS63l1dfW5nKBJhfZRIpHAP/zDPzAvZ7/fx/b2Nps6P1rVN0kyI2OAOkBvb2+jVqshnU5Dp9NhY2MDmUxmIpJZ6dlarRYEQUA2m0UkEmF5erlcDvfu3UMsFkM2m2Xlr+NSFXrUkAyKxSISiQQ2NzehVquxtraGvb09Vh0opxLsUSjxe3d3F9lsFuvr63jw4AHrYyWNDND5TXuPUgMoRYIa4x40W/CHcOiKjPSiIi8EuXDpIamiQuqmo/8rLWWTA1KPFeUQtdttaLVapFIpmEwmmEwmVuHV6/UQDoextraGWCyGhw8fskVBs07kIptWq4VQKAS1Wo3t7W02fBQAy2PodDrI5XITMRDydRGEp/Omnjx5wlzclOsw6rGaNHmNVlRSJ1/gqbtbFEXm4h7nHLPXQZo8SSH6Wq2GQqEAQRBgMplYZ1WpocT32TNIDnQGZzIZNmOJxoBQbojcZEZ7iRSOXC7H8mWi0ehQZ/5RaOxAt9tFpVJhxTyUFiCtVP4hyb2jHJoi8yJGK2roxVIZlvR7Rv+P3KBnajabyOfzWFtbw2AwYCE3UmQAIJ1Os14g5XKZWY5y2ix00VSr1RdagLSBpEqvXJ79MCCPAzHaNXuSIRkolUpWdUGJ9UqlEqFQ6LnvlSv0fHSJrK+vo16vs07j9Xodjx8/RqVSQbPZHMp34ICF4Ci0nclk8OTJE6TT6efagciV0WfrdrusmaTU+JRCIV3p+a1SqYbO8jfJiXnuNR70Q8xm89gF/8rl8rGtqB8in9fNch+9vA/rcDlO+ej1+lfK51Utvke9dkdNvV4/NvkYDIYfvL9IcZHyopDsYVOr1Y5NPlqt9o3PnxftN2ne3WHTbDZP7foZlcXoGXMcxsJxrR+dTneod9dohZL0Mj4smTUajWNbOyaT6XvJR+o1kZ49B91jhy2jSqXy0h9ypB4Zzst5lTvtsDfJaeWg55vU8MjrwuXy+kj326TKbdQCHpXDpMrl+8LPpVc/+3EqxwBXZI6dSVBOvg9cHj8cLrdXw9fXMJSHyPl+TPo6GvVonjZZcEWGwxlDTttBwuFwOCfFgTkyHA6Hw+FwOKcZnp7O4XA4HA5nbOGKDIfD4XA4nLGFKzIcDofD4XDGFq7IcDgcDofDGVu4IsPhcDgcDmds4YoMh8PhcDicsYUrMhwOh8PhcMYWrshwOBwOh8MZW7giw+FwOBwOZ2zhigyHw+FwOJyxhSsyHA6Hw+FwxhauyHA4HA6HwxlbjlWREQThPwmCcFsQhJYgCP/jOH/3OCAIgl0QhL8TBKEmCMK+IAh/ddKv6TTB18/B8PVzMHz9HAxfPy+Hr52DOWn5qI7598UB/FcAPwWgO+bfPQ78NwBtAB4AVwD8oyAIDwaDwdqJvqrTA18/B8PXz8Hw9XMwfP28HL52DuZE5XOsHpnBYPC3g8Hg7wHkjvP3jgOCIBgA/AWA/28wGFQHg8FvAPwMwP9zsq/s9MDXz8vh6+fV8PXzcvj6ORi+dg7mpOXDc2ROD8sAuoPBYFPytQcAzp/Q6+GMF3z9cN4Evn44YwtXZE4PRgDlka+VAJhO4LVwxg++fjhvAl8/nLGFKzKnhyoA88jXzAAqJ/BaOOMHXz+cN4GvH87YwhWZ08MmAJUgCEuSr10GwBPtOK8DXz+cN4GvH87Yctzl1ypBELQAlACUgiBoBUE47sqpU8lgMKgB+FsA/0UQBIMgCB8A+FMA//NkX9npga+fl8PXz6vh6+fl8PVzMHztHMxJy+e4PTJ/DaAB4D8D+Pf/+ue/PubXcJr5j3haupYG8DcA/gMvfRyCr5+D4evnYPj6ORi+fl4OXzsHc6LyEQaDwXH9Lg6Hw+FwOJxDhefIcDgcDofDGVu4IsPhcDgcDmds4YoMh8PhcDicsYUrMhwOh8PhcMaWA8ujjEbj2GUCV6tV4bh+l8lkGjv5VCqVY5OPwWAYO/nUajW+fg7gONePRqMZO/m0Wi2+vw7guPYXl83ByE0+vA6ew5Ex0qpEQTi2c5LDmRgOqvzle+54OLWKzOjimNQFQXKgz4IgTKwsOK/HYDBgH6NI1w9fR0C/3x/6u0LBo+2j8PX0cqQyka4lfk4f7x1+ahUZYPjyHgwGE7swpIfIpMqA88PgBsHLedHlPMnnzEFIZTV6FnF5DSt7pAzztXR8nDpFhhaDSqWCSqXCYDBgmm6/3z/QjScX+v0++v0+FAoFVCoVFAoFNBoNAKDdbqPb7QI42KU57tABID08SS5KpXLIciZFd9IhGXi9Xni9XphMJrjdbnQ6HSQSCdTrdcRiMZRKpaHvn9TDVq1WQxRFAM/WULPZRLfbnXiLms5h2m96vR5KpZLtPfr3breLRqNx0i/32BndOwqFAnq9HiqVCr1eD/1+H71eD91ud+L2GSlwKtVT9YLOapLLUXDqFBlSVpRKJTQaDfr9PlsMRyWE0wZtAkEQoFQqoVarYTKZoFAoUCqV2IU+KUgPTXruSb9oRpFazF6vF1evXoXP58PFixdRr9dx7949ZLNZ1Go1VCqViVo/L4IOWqPRyNZSv99Hp9NBp9Nh3zOJSI2HXq/HLmm1Wg2tVguVSsXOoHq9jlarNdHrSaFQQKFQwGg0QqfTodVqod1uDxmdwGR4aKReKZVKxe4wAEe6Tk6dIqPT6aBSqRAIBDA1NYVqtYp0Oo1ms4lsNotOpyPbxUAHCG0Ik8kEj8cDvV4Pt9sNhUKB9fV1pFIpVKtVVCoV2V7oUiuGvFIOhwNqtRr1eh21Wo1932gIctIYTeg1GAxwOBxwuVzweDxot9toNpsoFArIZDIQBAGFQgGFQoH9n0mBjCSlUgmn04lz586xC7rb7eLevXuIx+MTZywAz3t49Xo9TCYTTCYTVlZWYDQaYTQaIYoiM7bS6TQeP36MRqOBTCaDdrst2zNplH6/D41GA6/XC71ej6WlJTgcDkSjUUSjUVQqFdTr9YlRYIBnXk6LxYJgMAgAKJVK6HQ6SKfTR3Z/nxpFhrQ4m80Gk8mEDz/8EB999BHC4TBu3bqFXC6HUqmEZrMJpVIpu4VBHqfBYACn04mZmRlMTU3hrbfegs1mw5kzZyAIAv7mb/4GDx48wN7eHorF4pALT27QmtBqtdBoNFhZWYHT6cTW1ha2traYtw4YjtdPojIDPLMM7XY75ubmMD09jZWVFSgUCiwvL6NaraLX68HhcODRo0fI5XJD8pLbnhqFlF7y9i4uLuIv//IvYTabYbVa0Ww28d//+39HpVJBs9lEs9lk/28SkHrDaR2dP38efr8f/+bf/Bt4PB44nU4YDAZ0u130ej08evQIf//3f49EIoHf/va3aDQasjyfRyFvlVarxeXLl+Hz+fCHf/iHWFpawhdffIEvv/wS+/v7iMfj6PV6TCZyVWpob+n1elitViwvL+N3f/d30el08PjxY+TzedRqNZTLZSgUikOXwYnfgPTGUgjF7XYzS9JutyOXyzHtX64HCsmA3Lc+nw9zc3MIBALw+/2wWCyw2+0YDAZwOBxwOp1Ip9Oyt3zo/VapVNBoNPB4PAgGg2g0GqhWq6jX6ygUCuwAluv6eBkHVZGQUqNSqaBUKqHT6TAYDOB2u1EsFhGPx9mFJHV/yxmpwqZQKKBWq6HX62E0GmG329FqteByueByuZDP55kiI3dILnTZmkwmmM1m+P1+zM7Owuv1wul0wm63w2w2Q6fTsXwHq9UKm82Ger0OpVIp+z046inWarVwu93wer3MWwXgubCS3KE8GKvVimAwCL/fD7fbjXq9znKqjpITVWRIq1WpVDCbzTCZTPjpT3+KixcvMoUmGo0ikUggl8uh2+3KqjxS+uaqVCqcOXMGHo8HH3/8MT766CMYjUa43W4olUqoVCq0222cPXsWOp0OtVoNOzs7Q/k0clJqyENFCp7D4cAnn3yC69ev4/Hjx1hfX8fOzg6zAuv1Ovv+SWA0pCZVXvr9PjtIKcdBEARotVq89dZbWFhYgFKpRKlUQrFYRCQSGbIa5Yw0fk9KnkajYaHb999/HzabDTdv3hxSkgF5eqyk+8xgMECj0eDtt9/G22+/Db/fj0uXLsFgMMDlckEURQwGAxY+UqlUsNvtuHjxIvR6PX71q1+xdSRnBoMB1Go1dDodfD4fPvjgA0xNTUGn0yGbzSIajWJrawvFYpHJVm7nM0FGuE6ngyiKuH79On7yk5/A5XJhcXERiUQCn3/+OUsPoXNLdh4Z4KklYDQaYbVa4fV6MTU1xeKwnU4HtVqNCQGQ14EiCAJEUYRGo4HD4YDX64Xf70cgEIBWq4XRaATwrJLJYrHA6XTCZDJBo9Gg0+kwb5Wc5CJFpVJBrVbDZrPB4/GgWCyyHCFRFFlyppxlcBB0SGo0GoiiCFEUIQgCer0eS7AjuZjNZoiiCIfDAZvNxmQ3aUjd21QVqFQqYbPZ4Ha7odfrZXv5EHSekgJsMBhgNBrh8/mYJyYQCEAURajVagBAp9NBt9uFRqOBWq2GSqWCwWCAXq+XlZH5MqTJrBqNBjqdDjabDU6nE41GA+12mxlW7Xb7pF/usUERFZvNhmAwCKvVynI9ybA6Sq/MiSoyZBm5XC783u/9HgKBAC5cuAC/34979+7hwYMH2NjYQCKRQLPZZNbluCOtCjCZTLh69SqcTid+/OMfY35+Hl6vFzqdDu12G7FYDEqlEmazGQqFAsFgEBaLBeFwGLFYDNlsFru7u+j1eif8VEcHWY29Xg+9Xg9+vx82mw0A8N1330GpVKJarbJEMjlfPsCzhFW1Ws08MaIo4urVqwgEAlhaWoLL5UKj0cCNGzeg0+kwNzcHvV4PnU4Hg8GApaUlFAoFrK+vY3d3F61Wi/1sucsPGM5Jo1YHarWaGVRarfakX+KRQheyWq2Gw+GA0WjEp59+ivn5eaysrGB5eZmtlXa7jb29PdTrdRSLRTSbTczPz2N2dpZVEk5KYrQ0NyYQCMDn88Fms8FoNKJaraJWq6HRaKDVaj2XvydH6LzQaDSsKGVubo4ZVFRxa7FYkEgkjux1nJgiI3XxGo1GLC8vMyvAZDKhWCzi0aNHiEajKJfL6Ha77OAeZ0a1Uo1Gg+npaQSDQVy4cAFLS0vMQur1eigWiyzHQRRFFqP2er3w+XwsrCRnpIrfYDCA2WyG0+lEKBSCwWBAvV4f+j45I7WiyfNCCdHT09NYWlpiHoV8Ps9kZLfbWf6DXq+H0+nE9PQ0stksC1tOIqNeCY1GA61WyzwQcoYUYqPRCIfDgXPnzuHixYsIBoMIBALse1qtFrLZLMrlMjKZDOr1Omw2G6anpycuP03a58xiscBqtUKv17NWIa1Wi3mt5GxcjkIhWso3A4YNLlEUj9QJcSKKDFnWFosFCwsLmJmZwcrKCrxeL5LJJPb29vDo0SNsb2+z5l1yid9T5rrRaITL5YLf78fbb7+NQCAAp9MJpVKJ/f19RCIR5m3R6XS4fPkyLBYL/H4/TCYT5ufn8dFHH+HRo0fY2dlhrkw554mMdheVY6jxIKSXhdPpxMWLF6FUKlnOQqvVwt7eHiKRCJRKJct/MRqNqNfrcLvdeO+99zA9PQ2z2YxAIIBoNAqTyQRBENBoNNjhK2eZkvJHhywpLdLurPR9ckN6EZMC89FHH8Hn8+HMmTPw+XzQ6XRoNpsolUqIx+PIZrO4ceMGSqUSTCYT65VCoSW5e0GlZw6FuV0uFy5cuICpqSlYLBZoNBq0221UKhXmlaHGinKGPDJmsxl2u52FZHu9HprNJqrVKgqFAvL5/JEaSyemyHQ6HRgMBpw/fx6zs7M4c+YMrFYrPv/8c2xubmJ1dRU7OzuyTGQly3hubg6zs7N46623EAwGodVqoVQqEY1G8dVXXyEej+PBgwewWq1ot9tDFUyzs7OwWq3o9/v48ssvATyNX09CaOBFl80kPLP0zw6HA++88w7UajUqlQparRai0SjS6TTK5TLK5TK7iEwmEzqdDnw+H5aXlzE1NQWTyYRAIAC32w2TyYR+vy/7Kh1poiEl0NOH9N/l7GGgUJpKpYLJZILP58OHH36I6elpdqZ0u120Wi1kMhk8ePAAsVgMn332GYrFIq5cuQKfzzekyEwCo6X7brcb58+fh8/ne06RqVaraDQaslaICXpGk8kEp9PJcqUoV6hWqyGfzyOXyx1pj6FjXYW0GChJyu12Y35+Hh6PB7VaDe12G7u7u9jY2EAmk5FVyTU9O/VE8Xg8LB+IEqLi8TgajQY2NzextbWFXC6HYrGIbreL3d1dVKtVLCwssPwQo9HI3JpyCLu9CtoEdBhL82bksk5GGa1MovLgc+fOYXFxEUqlErlcDtVqFfv7+8jn86hUKkPNuLrdLmuEl0qlkM1mmVfQ4XCwiotyuYx2uy37qhOqBqSSYpPJBADodrtoNpvMu3mULdVPAgrnK5VKOBwOnD9/HoFAAF6vFw6Hg5UO5/N5pFIp7O3t4eHDh0in0yw3hqrfqEllr9dDvV5Ho9GQlaxGkd5dZrOZGZQWiwWCIKDT6aBcLiOfz6NerzPPppz3klThp/A1KTL0IcvQEl0+ZrOZNXn78MMPodFokE6nUSqV8PXXX+P27dtDi0EOCb4US6ZS4vPnz+NP/uRPYLFYYLFYWHOpUCiEr776Cr/5zW/Q6XTQarUgiiIajQZsNhvm5+eZ/Gw2GywWC0wmE1qtFgqFwpGVt50k0vJFaiVPc0w6nY6sD1CCDoVz587hvffew9LSEj799FP0+33s7+8jl8vht7/9LSKRCLMKSV6tVgtbW1tIpVLY2NiA2WzG1NQUgsEg8wiGw2GEQiFUKpUjaVh1WhAEAU6nE+fPn8eZM2cwPT0NvV4P4Gnvj1KphHw+j2q1OpSsOe7ykCb3qtVqzM3N4Y//+I/h9Xpx7tw5mM1mdkbt7u7i5s2b2NjYwC9/+UvU63VUq1WWvGk2m6HVaqFQKNDpdJDL5Vip+rjLaZTRULbRaGTVXLOzs7BYLACett+ntIhcLodOp8P2rNxkAmDIoAQAl8uFmZkZ2Gw25u0kBUav17NZXbIovyaXrsViQSAQgMvlgl6vx2AwQDqdRjabRaFQQL1el928E1LGaCM4HA5YLBbodDpUq1U0m03E43FEIhHkcjk0m012sPR6PdRqNahUKuTzeWSzWWi1WtjtdoiiyBJe5SKrUcjrIq2OoNLiZrOJVqslO+UNGLZ4yLIxGo1wOp2ssobK79vtNlqtFquWoEocWnedToe5vkulElwuF0u21+l07GKi3ytHaH1Q91Gj0cg8C+S5ajQaLFQnTQQed+hZRFGE0WhkbRxsNhvz5lJuRzqdRjweRyaTQa1WG5qRQ/lFdJFJ22NIe6bIDXomo9EIj8cDh8PBhkRSGKVUKqFQKDDPlRzlQIwmyavVamg0GpYzBTxzXFDTzaM8o49FkaGHpge9evUqfvzjH8Pn88FsNiOZTOKzzz7D/v4+dnd3WTdAORwgtLm1Wi1EUcSlS5fw4YcfYnZ2lnU+vH37NlKpFP7pn/4JW1tbqFQq7E2nhVGpVNDpdPDw4UM0Gg28//77CAQCMJlMWFpagsFgwP7+Pmq1mmw2ER2W5HmhaoB6vY5ms4lUKsVmmlD8Wg7PLUXaFNBgMCAYDGJlZQUulwsajQbNZhORSATRaBSpVIqNrZBOdQaeehvq9ToikQgMBgNsNhubbEz9Q+SeuEnP5na7cfbsWQSDQVb5Rd2iI5EItre3kc/nZeNhkFrODocDs7OzWFlZwdmzZ9n73mg08PDhQ0SjUfz2t7/Fb3/7Wxbup3UizSuifVkul1krfjk3LKWxDSsrK/j93/99zMzMwOVysZSAfD6P1dVV3L17F8ViUfZVb3Q/iaLI5gJaLBZotVrmNScFj0LdR+mcODaPDNWaazQa2O12BAIBWCwWZgWlUikkEomhDq1yOESAZ9nuNEzL6/XCZrOxke+ZTIZZQNlsdqhsT2otttttFoelZDIqzdbpdMfSCvokoFCSNCeGPAxU7ihHJYYQBIENNqRBftTnpNfroVKpoFwuM28MeRmkkGVUrVZRKpVYVQUA1khPajzI0cNFl5FOp4PFYoHBYGDPS/KhSgtq4DXuMhj1KlEuA/U+oXEDNJQ3FoshlUqxKhNSgChMQiED6iBNMpN6sOTEaHK4yWQaOr/JI0VdskulElqt1tivm9eFFFu626XJ39IUAPLIHNnrOLKf/K9Iy/3OnDmDQCCAd955B1euXEEqlcI333yDcDiMvb09JBIJlmwoh4Uw2v7bbDYjGAzizJkzUCgUSCaTiEaj+M1vfoNwOIxkMvnSuDwtCulFREmucpGXFGliL4WQGo0GU+Bo41B4hcJwcpMD8PQScbvd8Hg8LCSrUqlQqVSQyWSwvb2NUCiEarXK3P5SOdA67HQ62N/fR7vdxtzcHIrFIgaDAatCsVqtLJQgp2ZeFGajxN7Z2VnMz8/DarWyfyelOJ/PI51OM6/wOD+/9PyhMPbly5fxk5/8BMFgEAaDAb1eD6FQCPl8Hl999RXW1tYQj8dRq9WYEkPde81mM2ZmZrC0tMQ8enJUXqTQyAWHwwGr1Yr5+XnMzc3BbDYDACqVCu7du4dwODzkHZZzNaW0govWhcvlgs/ng9FoHMqfOa4qwGPzyNBhPDs7i6mpKQQCAdawi5IVK5XKC63JcYbcspTLYrVa4XK5UK/XkcvlkM1msbe3h1AoxJSTFx2g0suo2WyyUutJSHQlDwx9SK1D+pBzQzdShGlon9FoZDlC9Xod2WwWuVyOded9Gf1+H8ViEQqFguWiUYNBcguLojjUTlwOBzE9C+XG2Gw2OBwO6HQ6AMOWY6PRYNVbcvAKkxKn0+mYIbWysgKr1Qq1Wo1Op4N8Ps8SVTc3N4fa65OXTqvVspAkldlSDoRclRnpJWw0GmGz2WC32+F0OlnoqNVqIRaLIRwOo1gsotVqsbNJzkhzrmjwKo3NIY6zWeKRSVvar8BqtcJiseDixYu4ePEiRFHE+vo6iylms1m0Wq2xt4BGoXCaVqvF2bNnMT8/j+npaajVapRKJdy9exfhcJhN2qVDZ9SaBsCSqYLBIJaXl5lV3mw2EQqFkEwm2SUvFxmSZi9NZKWDghrBNZtN2YQBXoZCoWAhJb1eD1EUUSwWEY1GEQqFsLe3h3A4jEaj8VLvHK0jCg2QAkgXlTSJU07QQapQKJg1PTMzA5PJxHKCer0e8vk8C+1SqbEcDCrqAD03N4epqSksLi7C6/VCqVSyxN5vv/2W5VlJjSk6wzUaDc6dOwePx4OFhQUEAgGWGxKLxRCNRoeG+o77GpKuGZojdfXqVSwvL2NpaQk6nQ6VSoU1Ll1bW0MkEpGFF++HQucHfXS73aFQ21H3ODtyRUapVMLpdLJOiG+//TZisRiePHmC1dVV3L9/H9VqlSkycoE2gkajgcFgwMrKCq5evfqcIpNMJlEoFNBoNIYyvkehkfHSWTp0GIVCIWSzWdkl20kVmWazyWLxdAlLlRtKbpULo/1jaICoTqeDWq1Gu91GJBLB/v4+9vb2EIvFmKfqoJ8p9WSNWo7SEnc5Qc89OzuLK1euYGpqasgF3u12mSKTz+eRz+fHvthAWu0miiLm5+dx7tw51rer0WigUCggk8ng5s2b2N3dRTKZRKPRYOuI8tG0Wi1WVlYwMzODubk5BAIBRCKRIUWmXC7LYlyKtNyaPKFWqxVXrlzB22+/jWAwyKZcb21tIRQKYWNjA/F4HO12e+zXzfdFGsqm954UGaqQJEXmKKMHR6LISCtuDAYDFhYW4PV6YTAY0O12kUwm8fjxY4TDYTSbTVn1a5BCWd1arZbFEKmBWTabRTKZZI3/XmZF9/t9iKIIp9PJytb9fj+0Wi273KmaR07hAGA4M556EVC5/iSE1IBnLQtMJhPsdju0Wi0GgwGazSbr33FQIp20b4NKpYLT6cTU1BQLrVByK3UjlU7LHvd1RM+t0+nYMEibzcZCSpRAXqvVEAqF2IU87kjzEk0mE+vgS+30B4MB6vU64vE4EokEisUiarXa0Dk0GAxYqbbNZoPX64XH42E9d8rlMqLRKJu9JG2BMK7rRqrEAE/DJn6/Hy6Xi7XMUKlUqNVqyOVy2N3dZZ4YOeWUvSmjLSOOw0t16IqMNBFIrVbD6XTi448/xvT0NOx2O5rNJtbX1/GP//iPKBQKbAPJqQOi1DVJcfn5+XlcuHABiUQCoVAI29vbWF9fR6VSYZr8i34OuXaXlpbg9XpZeI4qmOgCOuox6ccNPTs14LJarazaol6vs0GRcocsarfbzWYkDQYDVCoV7O7uIhqNvjQ/aLR8VKPRYHFxEefPn8f8/DxsNhuKxSISiQQSiQRKpdJQI71xhvovqdVqpsD4/X5MT0+zbqw0liGXy+Hrr79mnk05QIqM3+9nc7neeustNo4in8/j4cOH2N/fRzKZRD6ffy6kRB7g6elpnD17lo22GAwGSCQSuH//PjY3N1EsFtkZNu7rhqDmpVevXsXU1BTOnj2Lubk5lMtlZLNZ7Ozs4KuvvmLd1+X2/K8LGZSjuTB03kirIY9SNkfmkRFFEXa7HS6Xi7VyrtVqqFarLA5NyYZyhKwTrVbLJldTOCSfz6NcLrP8DmI0N4Yalun1evh8Pvj9fpjNZmg0GlSrVeRyOZRKJbTbbdlaBHQhScv4yEtD+Uf07HLJkxkNK0mTLdVqNbtoyKNw0B6ivCsq36au0FS+3e12UavVmFUpzdOSgyxVKhVLUKWSa0rUbLfbbA5MPp9HoVCQTdI47QWNRgO9Xs9aNAiCgHa7jWq1inQ6zWbgUAWkdC3RGBm32w2LxcLCcZ1OB/V6HaVSiXVgp3Uz7ki9lxqNZsiLR0UF0lJrCqlNMi86f6jfF7V5OOqk8CPxyPT7fbjdbnzyyScIBoO4cOECjEYjfv7zn+PJkyd48OABotHoUI2+3JBaRF6vl1240WgU9+/fx/b2Nit1pcuKoIPBYDDA6XRienoaP/3pTzEzM4OZmRmIoohIJILbt29jbW1tKFlYDpcP8Cx5bDAYoFQqYTAYIJPJIJPJsMovn8+H6elpVjIrp9J9gvJY7HY7K28cDSEeFJYkK5s8WvPz8zhz5gxsNhtraBYOhxGLxVhYadwtS2nvFJPJhOvXr7O2B8FgkHWyzWQy+M1vfoP9/X2sra0hkUig2+2O/RqSesUdDgdcLhdT4ur1OsrlMnZ2dnDjxg1ks1mUSiVmDADPesb4fD58+umnCAQCWFxchNVqRb1eR6FQQDwex87ODjKZjGzCvCQ36qDtcrmwvLyM2dlZtu+SySQePnyItbU1bG9vM0VOTsr/9+VF1UnVahU7OzsIh8Mol8vMaXFUMjp0FZq8CNSK3+12s5KsfD6PaDTKLl7q9CdHyKOi0+nYIC3qiUIx6W63+5w2T282JQpbrVY4HA54PB643W6o1Wp2AVGiMLWplxvSZMx2u80+AAx5ZKg7q1whZX908JpU2SOkBwr9u1qtZjO9zGYzq9ih/jzlcpmFeOUCWdWUn+bxeGAymdhaIU9UKpVCJpNBpVJhFTtygM4faqJIa6ff76PVarGpxDSUVrpm1Go169bq8XjgcrlgMBjYhOdqtYpyucxkJievOimANNjYbDbDbDZDrVazfCpKCh/NDZpUpEocGVCU+kCpE+TxO/VVS3QAkCvy2rVr+Pjjj6FUKvHkyROUSiXcuXMHq6urqFarB1bojDvSHBmLxcLmmfT7fdTrdTahmEJCtAhIs7VarbBarVhYWMCnn34Kr9eLmZkZmM1mRKNRFItF3Lp1C9988w3y+fyRl7adNBRSkip+0qobqryR04EqRdpDSKrokueA+uxIk3QpyZVm6vze7/0e/H4/Ll68CL/fj2w2i0gkgtXVVayurjKFeNy9EdLBtF6vF7Ozs3j//fcxPT0Nh8MBACgWiygWi3jy5Alu377NElblkiwvTfYNBAKYmZmB0WhkfWOoZTzNVqK1Q0bBwsICFhcXcebMGSwvL8NqtUIQBNTrdTbY9uHDhwiHw+h0OkzRlovcjEYjFhcXMTc3h/n5eQQCAQBP1836+jq+/PJLpFIplhwt5+Z3L0Pa6NVoNLIy9X6/j1KphGQyia2tLVYJ1+v1jrS3zqH9ZFoEer2eTQedm5tDs9nEkydPEI/H2TwYQB6D2F4FWUQ6nY5N/qSGdtKhkNJs+cFgAK1WC5vNhmAwiIsXL8LhcLCW2KVSCZFIhJXeUjhAbpe4tHpCOprgRcPr5LyWaE1QjpDUPUtJdPQ90jwFsqwtFgs8Hg/OnTuHqakpeL1emEwmxONxpFIpJJNJJBIJVKtVWZTPAs9y9BwOB9xuN2ZmZjA9Pc1K9JvNJis9jkQiKBaLshtSS54FaU6U1CNDvZmk3hgao+L1enHmzBnMzc3B4/GwKq92u81CSlTtJMf9R2vH6XTCbrfDarWyburpdBo7Ozuo1WpDxqNc1s3rMOq9o6ReURRZAj3lwpKBJK2ePAreWJGRbgJBEDA9PY1r165hdnYWgvB0oura2hrrfDjuJXrfF6l3RqVSsaFtSqUS2WyWlVcrlUqWVLa4uIj5+XkEg0EsLCxAo9EwN+aDBw+wvr6OnZ0dpunKUZ7SRU8HZbFYRCwWY2XIVBFGeUak0I37zCly0ZIC02q1sLW1Ba1Wi4sXL8JsNkOv1+PChQtwuVyoVCooFosAnoV2Ka9mbm4OTqdzKC8mlUphc3MTN2/exPb2NuvjNO57U5qoabfbceXKFUxPT8NkMrEk6W63i52dHdy6dQtbW1vIZrPMGyOnXD3y1JHCRoYTtcSgmW9Wq5UpMFSeff78eZw7dw42mw0Wi4WdO4VCAXfu3GGWthy9EWSMz8zMwO/3QxRFDAYD1l8ol8sN5TZOKmQ0USI9rRWagXecXX2BQ/TI0OEZCARw9epV2O12KBQK1Ot1rK+vY3t7mx22k7IA6I2ky1WpVLLLhWYtdbvdIZcuNQ48f/48G1DW7XYRj8eRy+Xw6NEj3Lx5E7lcDvV6XbbJ0sCw5g8AhUIBiUQCi4uLTJExm81s4rfUM0XKwLhDiszu7i663S6sVisWFxeh1+tx7tw5+P1+9Ho91v9EWtFFU54NBgP8fj9UKhWSySTK5TI2Nzfx3XffIZvNolKpsEtuXPemdJ8pFAo4nU5cvnwZPp+P5ThQKHdvbw83btxAOp1mjSQB+XiJScGgjsV6vZ4pMmq1mikybrcb/X4fPp8PBoMBFy5cgMfjYR2Q6eekUik2Gfvu3bvY29tjF/k4K76j0BoyGAxsjI5Go2Hl6jTlulqtDnmGJw2p95cKL0iRoWpcCvEeVyL4oXhkqMyPkqMsFgvzIlAcVtpoaxKgi5RKzikXxmazYW5uDgaDAUqlkh0uarWaTQQPBoMwm81QKpWst8fq6iqbki2dsDoJ8iQPSy6XQyQSQaVSYRe22WxGuVweitHLQYGRKmL9fh+ZTAYAsLOzA7/fD41GA6PRCKPRiKWlpaE5SzTCgfIi+v0+i+lvbm4ik8lgZ2cH2WxWNn1j6BkcDgczFnw+H1wuF5MB5aYlk0mk02mUSiXZVNyMQopMNpuFSqVCoVBgAw1JsX377bcxGAzgcDig1WoxOzsLq9XKZnnRPDjKd6D8PEreHPc1AwyfFRqNBmq1GjabjV3OlBKQzWbZOBlKXJ1k6Plpory0V8xJnCdvpMiQZkaxWKvVCq/XC7/fj3a7jUKhwHqdVKtVZjHJHXoju90ucrkcswaVSiWmp6fh8/nQbrfxySefAHi2GKi6QK1Ws3k61Ar8H/7hHxCLxbC+vo50Oi1rT4wUaWXO7u4ucrkc3n77bSYvkiVVo8jhcB2l1+thZ2cH+/v7EAQBrVYLc3NzeO+99+B0OrG0tMR6o0j77kgrkra2tlAsFvHVV19hd3cXsVgMsVgMwLOqqHGUnbTUWqlUYm5uDpcvX8aFCxdw+fJlGAwG1vqAuveura1ha2vruZwruUB7pt1uY2dnB7lcDuFwGD6fjxUSXLp0CfPz88wgoHw+GhrabreRSCRw69YtRCIRfPHFF0ilUqjX62ymm1zOcloDRqMRdrsdU1NTWFhYgM1mgyiK6HQ62N7eZrKgcTqUvCqntfN9oTOXCi/oHKGvHdcddaihJY1Gwz6kDbsmEQp11Go1lsNQKBRYYpR0zo30zR8MBixngaaDJxIJVib6suGSk0Cn00Gr1WKVS6IospJiajgoJ5lIn4X2UqFQQDQahUqlQjAYZNYztTiQTnKu1WrM67K3t8eqCah0lKxqOawlulhptplWq2Uez2KxiEajwQoOpMm9coXOn0ajAVEUkclkEIvFMBgMWPKuwWAYUv4pv4EavYXDYYTDYZYMTmePHCEjmwaqUmt9aupGTfCazeZJv9RTAa2ZVquFRqPB5ipR3qa0gelxcCiKjCAITJulWJkgCCgUCiz5jkr/JiG8RJdDp9PB/v4+crkcbt68iUajgaWlJSwtLbEEO+DZkK10Oo1KpYKtrS3s7OwgmUyyMQahUIhdPuNqPb8ptDlIkbFarbhw4QJ0Oh2cTifq9Tqb3SUX74w0XDYYDLC9vY1IJAKn04kHDx7AZDJhbm6OXU4AmEVdLpcRj8dRq9UQDodZB19KxiNrSQ5yojPGYDCwpPlWq4VyuYx79+4hm83iq6++wt7eHpLJ5NgnNh8EPRMpcfV6HV988QW2t7dx7do1XL16lc1tA8DWBBVk7OzsDJ0/1HNGTqXWUqTl6lqtlinB7XYbsVgMhUIBGxsb2N3dRalUGnp+OcnhdaE7nRq8NhoNPHjwAO12m4W7a7Ua65dGSuJRyurQPDL0QjudDmq1GvtoNBoAJvMNHwwGaDQaEASB5bdQ/FWlUg3FFCkxr1gsIh6PY29vD+l0Gvv7+2g0GiiXy6zPh1xcuq+L9GCm5njUiIuG4lFITi4t5l9GvV5nOVcKhQJGoxEAXqrIJBIJNiBQak3KJTQwWgJLPXXIiq5Wq4jH40in04jH40gmk6hWqyf8qo8e8u5SeXU2m4VCoYDP50MgEECv14PJZAIA5gFOpVIsFyQUCiGTySCVSqHdbg/1MJLbWT76PDSCQRAEpNNp5PN5lEol1Go12XvyXgdp9KBerzOPH41z6Ha7qFar6HQ6x9bj7I0UGWkPAaqG+Lu/+zs8ePCAWUPlchmZTEb2TdtGoTearN/f/va3WFtbw9dffw2v1/tcUtRgMEChUECj0UAikWAt96m/BwDZhU5eF3pmKnu8f/8+fvazn8Fms2Fqagpmsxkul4ttHjmWRkpL0QVBQLPZRCwWg0qlQjweH1JKpHkylGRPis/ozxt36JnIU7e+vo5cLgez2Ywvv/wSrVYL4XAY1WoVmUyG7Sc5KHGvQno+x2IxNlPq4cOH0Ov1sNlsAMCqudLpNEvwpaTeZrM5lIckl3Ujhc5hMhYpvAaAhZO2trZYtdIk5CYeBHnl+v0+G3Hx2Wef4bvvvoPBYIDBYGD9qaitw6n3yNBGqVQqbCrx3t4ea1NM2pkcm7a9DnShhMNhRKNRhMNhmEymoTeVGlXRFGvqvCnt3kqemEmUIUGehng8jsePH2N2dpbNnqIOk0fZPfI0IE0kp0tG2tRM+j3SMIA0D0uOkLKfSqWQy+UgiiI2NzfR6/VYTgxVm9C+moS9RAYVGZWlUgnhcBiiKEKv1wMAG+pXrVZZozxpQquc1430uei5aYwMNQ+kCi4KrU2CEvwq6C6q1WoAgEajwfJktVotKzQ46kZ4xKGOKKDkVvJCSD8m4dB4EVKPC22I0QQoqRuYFB8abDfqtZlkSA77+/vo9/tYXV3FgwcPUKvV8OTJExSLRXYAy/XgJUg5eVkDQOnaOamSyJOAvE+03+gz8OzwlZa2y53Rs4MUYFJYgGdD/6Qz2+SuwEgZfUYKywLPlDzp+A/OM0geJKder8ccGMe5xw5VkZGGUoBnh+kka7DS/A4ArK/MQd8rLe3jPIPkQ54t6cEiXWOT4LmalPL77wtdOhTKBp6tjUm+iKTGEuV5HKT8TuL5Q/Kg8DQwfCZP8j02yujaoUIM+rfjvvePZLVKD4xJPThGoYPkIHlwWb0ek3whcQ7mZfuMr5dXy4Dvq2fw++v7I917xy23Q1Vk+EY4GC6fw+FV3gi5e2M4B8MvoZfDz6CXI5ULl9H346T33OT5DzkcDofDeQlciRk/BG69cjgcDofDGVd49hKHw+FwOJyxhSsyHA6Hw+FwxhauyHA4HA6HwxlbuCLD4XA4HA5nbOGKDIfD4XA4nLGFKzIcDofD4XDGFq7IcDgcDofDGVu4IsPhcDgcDmds4YoMh8PhcDicsYUrMhwOh8PhcMYWrshwOBwOh8MZW7giw+FwOBwOZ2w5VkVGEIT/JAjCbUEQWoIg/I/j/N3jgCAIdkEQ/k4QhJogCPuCIPzVSb+m0wSXz8Hw/XUwfP28HL52Xg1fPwdzkvJRHdcv+lfiAP4rgJ8C0B3z7x4H/huANgAPgCsA/lEQhAeDwWDtRF/V6YHL52D4/joYvn5eDl87r4avn4M5MfkIg8HgqH/H879UEP4rgOBgMPh/j/2Xn1IEQTAAKAC4MBgMNv/1a/8TQGwwGPznE31xpwAun9eH76/n4evn9eBr58Xw9XMwJy0fniNzelgG0KVF8K88AHD+hF7PaYPLh/Mm8PXDeRP4+jmYE5UPV2ROD0YA5ZGvlQCYTuC1nEa4fDhvAl8/nDeBr5+DOVH5cEXm9FAFYB75mhlA5QRey2mEy4fzJvD1w3kT+Po5mBOVD1dkTg+bAFSCICxJvnYZAE8kewqXD+dN4OuH8ybw9XMwJyqf4y6/VgmCoAWgBKAUBEErCMJxV06dSgaDQQ3A3wL4L4IgGARB+ADAnwL4nyf7yk4HXD6vhu+vl8PXz8HwtXMwfP0czEnL57g9Mn8NoAHgPwP49//6578+5tdwmvmPeFr6mAbwNwD+Ay/tG4LL52D4/joYvn5eDl87r4avn4M5MfmcSPk1h8PhcDgczmHAc2Q4HA6Hw+GMLVyR4XA4HA6HM7ZwRYbD4XA4HM7YwhUZDofD4XA4Y8uB5XUmk2nsMoErlYpwXL/LbDaPnXzK5fKxyUen042dfBqNxrHJx2AwjJ18arXascmHnz8Hw9fPy+GyORiLxTJ28imVSi+VD/fIcDgcDofDGVtOtOHRYDCAtPxbEIShzxzO6zLaRoCvIQ7n6JCe3ZN+bpMcSCaCIEycTEZlQJAsjloOp6pzIy0CDuf7Mrp5+FriTDKjSsZR/Hy+x4Yv7n6/P/RvkyYbkkW/3x969uOQw4koMvTGKxQKKJVK9jXpB+fZApDKo9/vD2m/giBAoVAMff/L/q/ckB6marWaKTCDwQC9Xg+9Xu/YLILTCB0qwLNDVqlUTqw8COnFQ3KRXvy0p8ZVTtLXfdj7n+SlUqmgVqvR7/fR6/WG/k3OSO8nOntEUYRCoYAoilAqleh0Ouh0OkOykSskCzpXRFGEWq0eOoObzSaTw1Gdxyfmken3+1AqldBoNACAXq+HwWCATqcj68v3h0IyITm9yK07jofuD0WqsCiVSqjVaigUCiaffr+PbrcLhUIBlUo1kdYjyYc+E2Q8TJo8pJAS0+12hxQ+hULBDKxxXDejFwW9/4f1DLTvVCoVdDoder0eWq0Wk+e4yev7QmuF5EBnj1qthtFohEajQa1WQ6PRQKfTOXT5nzbI+0L7xWg0wmg0otvtotVqodPpMEWGZEBKz2FyrIqM1BOjUqngdDqxsLAAhUKBTqeDbreLaDSKUqnE/g5M7oErPRjIQjQYDFCpVOzvtGBI+5daC8CzMItckMagNRoNjEYj9Ho9gsEgNBoNut0uer0eCoUC8vk8Wq0WqtXqRFiLo4iiCIvFAoVCwQyGarWKdrs95I2YFKSXj0qlgiiKMBgM7DwSBIHtpUajgWazyf7fOCBVYhQKxaG+bvpZ5HWYnZ3F3NwcCoUCQqEQ2u02yuWybC9uen61Ws2UOLPZDK1WC7fbzT4bDAbk83nk83mkUilsbW2h0+mc8Ks/XKTrivbO1NQUHA4HvF4vfD4fO3er1So2NjZQq9VQr9eZt6rb7Q5FE96UY1Vk6GIWRRFarRaLi4v4i7/4C4iiiGaziVqthl/84hfY2tpCtVplC0Bum+JVSENspPGS1u/z+WCxWCCKIlQqFcrlMmKxGDqdDmq12gtdmXJRZqQXr0KhgNVqxdLSEtxuNz788ENYrVa0Wi10u11sbm5ic3MTqVQKT548YdYRIP/1RGvHYDBgenoaOp0OHo8HCoUCa2trSKfTaDabaLVaACZDHsAzL4xGo4FOp4PL5cLi4iK0Wi3MZjMUCgWy2SxqtRrC4TAikQjz2gCnW05SBV+hUAyFfd40n4U8emRIGQwGfPjhh/jpT3+K9fV1/OM//iMKhQJqtRo6nc6hXU6nCbq7TCYTrFYrfD4fLl68CIvFgqWlJVgsFiwvL8PhcGBvbw97e3u4c+cOW0NyU/BoPajVauj1erz77rs4e/YsLl26hIsXL6JWqyGdTiOZTOLnP/85kskkYrEYyuUy8vk8arUalEolRFE8FJkcqyJDL9hoNDLtzePxQBRF1Go1aDQaaLVa5m2Q0xv/IkYVDKnHij7IArBardBoNJiZmYHVamWKTalUgiiKaDQaSCaTaLVaaDab6HQ6slFgCDogTSYTDAYDvF4v5ubm4HQ64fP5YDab0W630ev10Gg00O12odFoUCgUUK/XUSwW2aU0CWg0GtjtdlgsFszOzkIQBGSzWbTbbRSLReZxkOseG1375E1wOp2w2+1wu92YnZ2FVquFyWSCIAgwmUyo1+totVooFApMMR6ns0ga/iAO47UrFAqYzWbYbDa4XC643W7E43F2VskRaR6MUqmEy+WC3++H3+/H7OwsTCYTfD4fjEYjzGYzDAYDbDYb6vU6XC4XnE4nyuWybM4ekodKpYJGo4HH44HFYoHf74fX64XdbofRaGRRll6vh6mpKWi1WoiiiFKpBK1WC6VSyaIJh7G3jkWRkYY5FAoFVlZW8N5772FpaQnXrl1jllCxWITZbGZuXrky+mzShMzBYMDijFarFVNTUzCbzTh37hzMZjMWFhZgt9uhVCqhVCpRrVaRSqWQzWZx48YNZDIZbG5uIpvNHqrr7iQhr5RGo4Farcbly5dx5coVzMzM4O2334bBYIDD4WBJZoPBAOfPn0etVsPu7i5mZ2eRSCTw5ZdfIp/PyzqWL/XmORwOvPfee/D5fHj//fchiiKsVitWV1fx6NEjZDIZ2V5C0rwX4Glc3ufzwWq14u2338b169dht9sxMzMDlUrFzpxut4tut4tf/OIXUCgUSKVS2NzcHEocP42Q0UJeR/r8pp4Y+kx5MRcvXsTy8jLefvttnDlzBqlUCoD8En2l+0ihUMDlcsFsNuOTTz7BRx99xNIiyKCkfdTv9+FyuWCz2QAA8XgciUQCv/nNb1AsFk/2od4QqUfTZDLB7/fj3/7bf4tgMIjFxUU4HA7o9Xq0220olUqm1Pzpn/4pMwzq9TqePHmCjY0NhMNh3Lx5c8iL90PX6rF7ZARBgNFohMfjgdPphNlsxmAwQKlUYg8jJy/Cy3iRtUSHqdFohN1uh8PhYIfv1NTU0GeVSgWlUolarQa9Xg+DwQC3241+v8/cdaf10P2+0CFhMBhYmGRmZgZTU1MIBALMWpJWLZlMJhiNRjQaDQSDQQBgf282m2NnZb8Oo5U3Op0ODocDTqcTXq+XKTJGo5Elssp1r0nzyuiyke6pYDAIs9kMq9U6tAY0Gg0UCgXcbjdcLhcajQa7oMaBF+XIHcbPo6RWh8MBv98Pq9UKnU4HlUr1XH6eHKCzgZ7barWyZ5+dnYXVaoXT6YRCoUC322UhyF6vB7VaDa1WC5vNBp/Ph16vB41GA5VKNfYhJlJo9Xo9LBYLZmZmMD09DZfLxTwx0kpSpVIJt9uNXq8Hk8mEZrOJer2Oer2OWq3GZPKmHKsiQ14Ei8UCr9cLq9UKhUKBSqWCR48eIR6PY29vD9lsllWcyBHpYaNQKKDT6aBWq5mScvHiRVy8eBFGoxFOp5NtDKVSCZ1Oh36/zxI4KVvebrej2WwinU4jl8uxpM5WqzWWSg1ZlyqVimn2H330EWZnZ3H+/HmcO3cOCoUC7XYbuVwODx8+RKlUYrlVc3NzmJ2dhSiK+OSTT5DJZNBqtRCPx3H37l3E4/GTfsRDRxrHNxqN8Pl8mJ2dhd1uR6vVQr1eRy6XQyaTQbPZlKU3hpQzshrNZjPOnz8Pm82G8+fPw+PxQKfTYTAYYG9vD19++SWazSZKpRKUSiU++OADzMzMwOfz4Y/+6I9w9+5d7O3toVarod1un/pLSFp2fRhKDF3CHo8HNpsNb731Fq5fvw6TyYRcLsfWU7FYfK5/yDgiVWD0ej0WFhZgs9nw8ccfY3Z2FvPz85ienkav10MqlUKz2UQymUS73YZOp4MoivB4PPB4PHC5XHj//fcRDoextbWFaDSKZDKJSqUCpVI5VnuP7iqFQgGPx4Pr169jenoaZ86cgcfjYYY1fS+tA4VCAa1WC+BpaLfX60EURUxPT8NqtWJjYwOlUgm5XI55Zn7IGjp2j4xSqWQxaXrATqeDRCKBcDiMYrGIWq0my0P2RVD8VavVwufzwev14tKlS/jggw+YnAaDASqVCnq93lC1CeXQkCzn5+dhMplgt9uh1WoPxb18UkgTnfV6PaxWK0smm5ubw9zcHAurlctlrK+vI51OI5/Po9FooN1uw2g0sjwaSgzW6/XY2toa8t6Mm2xeBj2PKIrsErfZbEPlkGQJtdtt2Tz3KGQ1koJPh+2lS5fg8/lQKBRYVduDBw9QqVSQSqWgVCoRDAZhtVphMpngdDqRzWah0+mGeoMApz+v6DDLrSmxnvJDZmdn2TqqVqus3HhcvFYvQ3oe0AUsPZNXVlZgNpthsVhQKpWQyWRQqVQQDofRbDZhs9nYmQ089QDPzMwAABwOB6rVKrLZLDNExwWpd0+pVLLnCgaDcLlczIh+WY8zUnAo9K/RaOB0OpFKpWCz2ViV6Ztw5IqMNM5oNBpZMpTVamWlj81mE7u7u9jZ2UGlUpGVixIYTuod3SxmsxnXr1+H0+nEtWvXEAgEEAwGYTAY0O12kUqlmMeqWq0yN6bJZILJZIJOp2OJwDabDWq1GufPn0ev18PW1ha2t7dP/aErheRDCprNZsO7774Lj8eDc+fOYWZmBs1mEw8fPkQymcTa2hqy2Sxu3ryJUqmEer3Owka5XA4XLlxAIBAAAJw7dw4ulwsPHjxAIpFAs9mUVcIrPYPNZsPCwgJmZmbgdruh0WiY1yGVSiEWi6Farf5g6+e0IwgCHA4H3n77bXi9Xrz33nuwWq1ot9vY39/H6uoq1tbWkMvlsLOzw0qHVSoVNjY2AABnzpzB8vIyrFYrrFYrK8mWe4MzYPjM1mq1cDqd+OCDD+D3+yGKIqLRKKvqevToEVqt1thXBNLzCoIAp9OJM2fOsOd2Op2Ynp5mpdWhUAiJRAKrq6sol8tsDXm9XpjNZrRaLRiNRiiVSnbfTU9PAwCSySSKxeJYyYnCYX6/H4FAACsrK7h06RKcTidUKtVz1cX9fh/1eh2FQgG9Xo8VnjidTuj1etb+IBAI4Pd///eRTCZx48YN5HI5lMtltFqt7302HWuyLyWwShUZQRCYIrO1tYVKpTL0f8YdacM6aUk1hdnMZjOuXbuG2dlZvPfee8xt2e/3WS+CZDKJX/3qV8hkMqwHCB2wZrMZgUAAbrcbv/M7vwOn04lz585BrVajWq1ia2vrhCXw/SB3tk6ng81mg9/vx49+9CMEg0GcPXsWXq8Xa2trePz4Mba3t/Hll1+iWCxid3d3yCosFArY2dlBp9PBxx9/DJPJhJWVFQQCAXz22WcwmUxsw8ntQrfb7Zifn8fU1BRcLhcAoFgsolwuI51OIx6Py7ZMlnA6nbh+/TqCwSB+9KMfQa/X4+uvv8b+/j6++eYb/PKXv2TNuii/QRRFbGxsoNPpwOfzsXPKYrGwEKacPHgvY1SRsdvt7GxqNBqIx+N4+PAh7t69yyolpd6YcZOP1LhUKpVM+fX5fPjkk09gt9tZHl4oFMLa2hq2t7fx1VdfsbOn0+lgZmaG5X3Ozc3BYrGwc5ry9NbW1saqKpfO416vx+6Wc+fO4dKlS0wpeVFIqFqtYn9/n/WTAZ4ZkjqdDlqtFsFgEL/3e7+HSCSCUCjEdIF6vQ7gWePO1+HYFRmn0wmLxcK6QiYSCaRSKdRqNbYhxjGn4yCkShnFEqliwuv1YnFxEV6vF2q1Gq1WC8lkEslkErlcDru7u8jlcohEIiiVSswjU6vVkM/n4XQ6IYoiiz9SaRxl0o8L0oZbKpUKfr8fly9fhsfjwezsLBwOBztENzc3ce/ePcTjcdb3g9YNPTO1xqZS9F6vB71ez/KKpN0lx11plpaIKhQKOBwO1qAKAJrNJsLhMBKJBIrFomx76kg9nTqdjl0qtVoNzWYTe3t72NjYQCqVYjKQNpETBAHtdhuNRgOtVouVXk8Ko8niVqsVs7OzmJmZgcPhgE6nw/7+PlKpFHZ3d1nz0tFS73HFarXCbrdjbm4OCwsL7Gzt9XqIxWKo1+tYXV1lHt1sNot6vc6evVarAXhaqRQKheD3++HxeFheaLVaZfmQ49AJmZ6L7hPqn+NwOKDVaqHVaqFSPVUhyJOSzWaRzWbZ3dVsNtFoNJgXplarwe/3w2AwsChCtVqFw+FArVZDMpn8QaG3Y1NkBEGA1+vFwsIC/H4/HA4HkskkVldXEQqFWJOcXq8nKwt5NKSk1+thNBpx7tw5/Mmf/AncbjeuXr0Kk8nEXNy3b9/GjRs3kE6nsbGxgUajgUKhwMY3kJbc7/cRDAbZ17rdLlswlCU/LnIkORkMBlitVly9ehV/9Vd/BYfDgfn5eahUKqyvryOVSuFf/uVf8M///M+siaJ0w9Gf2+02SqUSKpUKGo0Gq3iiC3+cZPO66PV66HQ6zM/P4/r166wEtFwu4+bNm9jf30csFmOVOOOk6L4KaXUNXRzz8/PQ6/XI5/OoVqv4+uuvcfv2bWSzWTSbTWaBSystGo0GSqUSyuUyqtUqaxo4KZAnhvKF/vAP/xA+n4+Vqa+vr+P27dvY3d3F3t4ekyExTntqtHI0GAyyhm6ffvopNBoN+v0+ms0m7t69i1AohFu3buH27dss30zaV4WSn9fW1mAymXDhwgWsrKywRqaCIMBsNkOj0bB+V6dZXvRs0mKUCxcuwO/3w263MyWm3W4jkUggk8ng9u3buHv3LgqFAiKRCDqdDlqtFushs7y8jHfffRdTU1OszYggCJiZmcFgMMDu7i7TAb4Px5YjA4CVbOl0OiiVSgwGA2b50MUsV8jKMZlM8Hq9rIGQw+GAKIoYDAbI5/OoVCqIxWJM4y+VSmi322zhA8PuPmm3TTpQOp0Oawh32pEqIQqFAjabDcFgED6fD06nk5VM93o9RKNRRCIRpNNpli9EMpH2IRhVHqUWo9Tbd5oPke+D9MAxm80wm80wmUwQRRGtVgu1Wg3FYnFIGZYjdAnT4DryShaLRRSLRZRKJZRKpaGOxqNrgSxl6TydSUDad0ej0bDeTIFAAFarFeVyGd1ul1nbtVqNVZaOs+FJxQRUbUThWEpMpYTeWCyGaDTKPMDkGaefATyrGmy1WqhUKkMJsKQcjouspOel2WyGXq+H3W4fSgkZDAZMUclkMohGo4jH46wAgwpUpPcQGZDSUB4Z36QY/RCOxSND8WdKFHK73VCr1QCARqPx3ICtcUcaspBe1NRQ6uOPP8b8/Dzeffdd1gumVCrhl7/8Jba2tvDo0SM8fvwY3W6XXTyjlg/NuPD5fHjnnXeYu24wGCCZTGJ7exv5fP5Uuy6BZ83uKHH5vffew8cff4xgMIj5+XnU63Xcvn0b6XQan332GTY3N1llkjSU9KIwEV1UcvI8jCLNuQoGg0xufr8fzWaTVQNubm4iHA6jWq2e6vXwQ6G9RrlVFosFarUa7XYbT548QTKZRCgUQjabxWAweOngOvJsSpUZALJfQwCYMenxeHDmzBlcv34dv/M7v4NarYZ/+qd/QjQaxTfffIOdnR1WRguMp0FA+0atVrPcuU8//RSffPIJRFFEp9NBPp/Hz3/+c8Tjcdy6dYuFl9rtNgA8p5SQZ6/RaCCfzzPlT5ouMQ5GlPQMVavVuHjxImZnZ/H222/j7NmzLNxGHcJLpRK+/PJL3L9/H9FoFNFolBnb1CZEp9OxdhAWi+U5A/RNORZFhrRRKk3TaDRMK6PNIyfrR/oc0vwFURThcDgQDAbhdrthNptZUm+lUkEymUQ4HEY6nUapVALwbLNIE8SAZx4MspysViuUSiVzhY6DW1yq9VP1ldvtxvT0NOte3O12kU6n2QahuVIUR33ZRiCZkdxIdrTW5BLXB57JUa/XszbpVKlUrVZZiI28WHLLQQOGq93o+elioRbxzWaTdR19mWX8IuV4UpCG5agPCoW8U6kUotEo685K3zuuMpI2u7Pb7fD5fPB4PHC73WxuHc2xIy9wLpd7bt7b6M8Enhpn7XZ7LDziB0F7wWq1Do0fAJ7uNwrfU1FKIpFAPp9HvV5n8qWfoVKpoNVqodfrWY7QYTbAPTJFRmop2u12mM1m9lkUxaFeKKPa6jgjVTaAZz0t5ufn4fP5cPnyZaysrLADolgs4rvvvkMikcCdO3fYwEzyvox6GugCdrvd8Hq9WFlZwdLSEsxmM3PzlUolFItF1ivkNMpVOnZAr9fjgw8+wPLyMq5du4aVlRUUi0V8++23iMfj+PnPf45EIoFoNMqakr0so13aS4VCbtSBlPpeUNhNLqFMUpbpwDGZTFAoFCgUCmxwHR0yPyT+fNqR7jfqFzM7OwuNRoNarcbKhal6AhhWUqRVKzRAkjyE5HWQy1qRIj1TaKK1zWbDj3/8Y3zyySdQq9V4/PgxotEo7ty5g3A4jHK5PFYhklHIeNZoNCxs9vHHH+PChQusC28ymWTehW+//RbpdJpV077KoyLN1ZLKSBruln4+rZAiotPpsLi4iOvXryMQCEAQno7xoOar//AP/4B4PI7V1VWkUim0220WbQGeRQ5EUYRer4fJZGL/LnVeSEObP4Qj9cjQ4UAJrnq9nmU6Sx9iHDfE6yB1S7tcLkxPTw9lsvd6PdTrdYTDYYTDYdb58UWK3YuShqUdJKkBHoXqyPo8bQiCMBQ3VqvV0Ol0mJ2dxYULFzA7Owu3241yuYz9/X2Ew2FWaSJNkHvVmiELgJKelUolG6hJ83Rofcpl/VGODHk8G40GYrEY4vE4S3omT55cGL0YaIgfefT6/T6KxSLy+fxr7Qfqok15Nt+nBHQckSpx1IJ/cXER58+fRz6fx/b2NiKRCFOGKS9mHPcMnT3kDSDFf35+HisrK2y9lMtlVpUViUSQy+VeywMlPe9fFEY67coLMPwayWPldrvZSA/ycpI3Zn19HaFQCMlkkoWt6X4nY5WUOlEU2Xks/X2kXL6JfI7cI6NQKGCxWOBwOFgiIl0qtVoN2WwW+XyeJfye9pyOVyE9WGlqNfWKOX/+PObm5qDValEsFrG/vz/UMl/aKOlFmjz1ODAajXjnnXfw9ttvMy25VCrhzp07SKVSePToESKRCGt6dpqgNaFSqWAymXD58mW4XC5cuXIFKysrUCgU2N3dxerqKm7cuMGGiUrbw79ofdCmoYqkhYUFnD17FhcvXmQx2Y2NDWQyGSQSCTbCYZwVGVobdPnS/BdqbUBN8CqVClPcxuEw/SFQEqHZbIbb7YbFYgHwNO+jWq2yxMOXvd8kF41GA61WC7VazS4+6hQtJ9lJc0TsdjtMJhM++OADrKysYGpqCtVqFXt7e/jVr36FZDLJWj+M8/lMnieNRgOXy4W33noLfr8fPp8PWq0WkUgE0WgUm5ub+O6779i99LoVfpRc7vF4WN4NeYLj8ThisRgLcY7LOAc6U6lyifZEp9NBvV5nnnLKWZTeWzT3b35+Hm63G4FAgA2WBJ7mx5bLZUQiEezu7rIOyT/E2DpSRYasbovFwjKeqeNhu91GpVJBLpdDoVAYSooad+jZVSoV3G43HA4Hrly5gnfeeYd5pWq1Gh4/foxQKIR79+4hlUo9V1kz6npTKBQIBALw+/1499138bu/+7tM2y0Wi/j1r3+NUCiEx48fs5brp0mRkVp/NMDw+vXrmJqawuXLlzE/P4+dnR3s7u5ibW0NX3/9NcvtoEPoIJcuhZS0Wi0WFxfx4YcfYnp6GmazGYVCARsbG4hEIojH46hWq7LwyJAFpNFoWBt5rVbLRhKQIiOXRPpRpDlj1GDS4/HAbDYzNzgpMgfNb5OuH6pioWGRzWYTrVZrrC9xKdJqPjqjPB4P3n//fVy/fh3VapU1NPviiy9YQmen0zl1Z8rrQs9M3XbpTKZeXjqdDslkEt9++y02Nzdx69Yt9p6/rieGjCiPx4Pl5WX4/X7WMC4ej2N/fx+lUgnNZnOs5ChVZIBn+TGNRgOJRAKJRIIpH6NRBFEUMTc3h2AwCL/fD6fTCeCp16peryMajWJ/fx97e3uIRCKs58ypUWQOgioDSJmpVCpDCZzjfrkQFIf1+/1sDsdgMGAzN3Z3dxGPx5/T0KUXvlqtZhaE0WjExYsX2aJQq9VsTkwsFmNN9E7rHB16Lnoe6k8RDAbR7/eRy+Wwt7eHhw8fYm9vj1WzHWRFS+WkVqsxOzsLp9OJpaUlTE9Pw2KxoFKpIJ/PIxaLscqDcW+8KLWoaYozef8AoFKpoFQqoVAooFwuD+0tuSL1ZtIZQ60IXvaeS3MaVCoVdDodDAYD2u320OyuZrMpC0VQamTRLKorV67A6/WyvkOJRALb29vY3t5mZcRkSI3r+pHmxkjbX7hcLrRaLaTTaUQiEWxvbyOVSjHv0+v+XGrCSEqS2+2G0Whk649aAFB48zSvJWnYX6PRPNcnqNlsskiKtLGmND+UzmSqVgoEAtDpdOznkKczlUqxdhp0D/6QdXYsisxoGSP1QKlWq+zypdCSHMpl6U01Go1s/MDU1BRMJhNb0Lu7u/jNb36DQqHAPA7SLG5yy+l0Otjtdnz00UfweDz48MMPMTMzw+L4uVwOX3zxBeLxOB4/fsxyAaRWxGgC8klBm95gMGB5eRlzc3N455134PV6kclksLOzg6+//hqfffYZu4DpkBjNVSAZkaeG8rDef/99nDlzBleuXMHFixeZore/v48HDx5gf38f+Xx+7C92kqVWq2UHcjAYRCAQYB0yY7EYQqEQU2TGucrkdZEeqNJLhIbSvUh5Ja+WKIosDF6r1fDkyROEQiHWN0UOHrx+v8/2IA1U/fM//3NMTU2x9g2rq6v4P//n/yCVSiGbzaLX67EckXGF+pmYTCYW4r9w4QKcTicrz7979y5u3LiBTqfDFJlX3UWkMFMPLLfbjbm5OSwvLzOvQz6fZ3lG0gaepxmFQgG9Xj/Un4zWfq1Ww/b2NkKhEAu9EVLPF3mJL1y4gPn5edjt9qHvKxQKWF1dRSwWQyaTQblcHvo934cT8ciQIkOdWaVtnsf5kJCWgFKpudPphMPhgEqlYolk8XiclVhXq9WhQXR0aatUKhbvp6FlLpeLJXPSAU3JnKlUinkwpBvltMiTDgUaHur3+9lAQwDIZDJsrhRNQH/RmpA+G4UTaG6H1WplHjC9Xs/CCpFIBLFYjA2VlNPgP5InNa2i/CsKKVHSt1zCIqOMxuQppCadVE0K74u8MbQuyWigMLi0LYIcKtxGzyaHw4GFhQVMTU3BarVCr9ezfUfNOMnAkgP03qtUKlbNSOcHjXspl8tsXtvr7BdpmFytVjMvs91uh06nGwrt0j132rv5As/OapPJxO4bhULBlDua2UYGoXQPkpz1ej2cTicbpGk0GlmSL+3VZrP5XGrJDzUuj7xqSZqVDDw9eCuVCsrlMlKpFEtylYPFSNaOzWbD0tISZmdnceXKFQQCAYiiiHK5jHv37uHGjRssK16aREhWj8fjgc/nw9LSEn7yk5/AZrNhfn4eWq2WDbqjVtDhcBh3795Fo9FgIx6A05UpTweDwWCAXq/H2bNn8Wd/9mewWq1Qq9UoFAr4xS9+wZ4nEomwTSG1AinPgxQik8nEytD/6I/+CF6vF2fPnmVVT5Rr87Of/Qy5XA6hUIh15Rz3tUaIooipqSnWm8hisWBjYwOPHj3Czs4OCoUCWq2WLDydB0Eu+2KxyCr/AoHAczO4RqHwHOWyXb16Fe+++y6++eYb3LlzB3t7e2zm0riOtaDzt9/vw+12w+fz4cqVK/h3/+7fwWKxwOl0otvt4vPPP8ejR4+wvr6O7e3tIY/EOD43IQ2VGI1GuN1uVtXWbrexs7OD1dVVRKNRtlde1WWWznpSYMxmM3784x/jwoULWF5ehsViQTQaxfr6OsLhMPb395FIJJ7zlp82pCG4M2fOwO/3s7y7QqGAQqGAx48f49e//jXrGQM8a0+g0Wig0WiwsLCATz/9FF6vF0tLS3A4HCzHhqpGU6kUbt26xXoTvYlMjlSRkYY2pM3J+v0+G01AwyLlYDFKE+jMZjMsFgvrSdHpdNDpdJg2Kx3eR5NVKaHK4XDA4/EgEAhgbm4OZrMZNpsNKpUKqVSKhUtonHwul2Oj1IFh19xpUGQIsnxpTAO5spvNJlKpFMLhMKsUICVG+hxU+kdlfGazGS6XC16vF9PT0/D5fLBYLBBFEc1mk1UoRSIRFItF1Ov150bOjzMkI/LIUDyb+hORN6HX671R++/TjDQpnsJJpHi8au1Lw90Gg2FozwJAqVRiA0lP0z76oYxaytPT08w4osqaUCiETCbDmt5Rpcq4I/XIUFsG4OkFXK/XUS6Xh3I0DkJqZNHgQ7vdznJvjEYj8zjQEMV6vT6UC3naZUqpEdT3TaFQsPurVCqxUJC06R95pyj3KhgMwuPxsAaVwLNQHEVjisUiC32/CUeuyJArz2g0srJG6ndCCYm1Wo0pO6f9DT6IFyky1JCN4q5kGanVaqacBINBGI1GLCwssPkmwWAQNpsNfr8f3W4X6+vrbKBkNBrF7u4udnZ2xmb4GCUYajQamEwmeDweKBQKbG5uIp1Os2RlUmpJlrTpKQZNDZrm5+dZQ0CTyYTZ2VmIoojHjx8jl8thdXUVjx8/Rjabxf7+PpP/uCMNh1CiL40mUKvVrFHV7u4uMpmMLAyE14GUDbL2pJ5J8nRKK7ekl4ler8e5c+fYmA8qQshms6hUKmMrQ2nVo8FggCiKuHTpEj755BMEg0HodDqUy2X84he/QDwex7fffotQKIRmswlRFMfiwn0Vo8ncBoOBNWYVBIHlzrTb7aHLVPqeSxVl4Nn8IfJW/OQnP2GNGD0eDyqVCh4+fIhHjx7h//7f/4tCocCKMMZhLUlDZhqNhhVSZLNZrK+vY3NzE9FodMgJQXf7hQsXcOXKFSwsLODDDz9kXnhp9W0sFsP+/j42NzdZku+b5p8diSIjfVFkhVMjvNFhkaSparVa2bi+FQoF0/rVajWzhqVDxqiVuk6nYx0mL168CL/fz/I8KAGxVCohmUyyjpNbW1ts0ir1ZJF6v4DT5Ykh6FIhrb3X66FcLiOXyzEPAn2f9PAgzwyNMZiZmcHly5fh9/tx8eJFaDQaNiAxlUpha2sL9+7dw927d1mZIIBT7dL9vkj3FVUtkdVEMX9pJ9tJgJRfMhboDJJ+SA9U4NlepHCuKIrMYhyHMR+vgs5i6qDt9/uxsrICq9UKlUqFRqOBx48fY29vD+FwGNlsduwTe0chxZ88TDqdDlqtlq0H6aDQ0f8n/bPUW6PVauF2uzE1NYUrV66wRqcGgwG5XA7xeBx7e3tYX19HtVplVXOn/Y6T7guSGZVD1+t1lgBOZeTkfKBz3ev14vz585iZmcHs7CzUajXLVyNjo1QqsUaD5D19Uw5dkZFa33q9Hm63G++88w6mpqZYDXk2m8Xm5iYSicRzeRDjDB2W7XYbhUKBTY01Go1Qq9UwmUw4e/YsALDLRhRFTE9Pw2g0IhgMwmKxQKPRYDB4Onk1Foshm83ixo0byGQy2N3dZXkPpMCM4+UsPUAAsGx/aRksHbak7ZOXanFxEQsLC0xOuVwO6+vryOfz+O6771hyb6vVGjo8xlFOL4IsIMp3IBcujabY399HNBpliuG4ezpfB9oHNLDP4XAAAJtvRjNhKKYPgE30nZqaYi3qy+UyVldXsb29zRp9AeM3NFLqiSBPzNTUFM6fPw+32418Po9//ud/RiKRwNraGisWGOfxAy9DEATmpaPcTMrX0Gq1mJ6eRr1eR6lUQjweZ14aaejRYrGwUmKfzweXy4XFxUXY7XbMzMzAYDAglUqxIbf3799nTUkpf2scZEr7qN/vo1qtolwus4pit9uNK1eusGRmacsUvV4PjUaDS5cu4ezZs7BarUMtInq9HpvVdefOHfzmN79hZe7S3/1DORJFhhJ3zWYzvF4vrl+/jrm5OfZw2WwWGxsbSCQSAORz0JIW2+12WZk1ZaybTCYYDAasrKzA5/Mxq08URTidTjYhVKVSsSZwuVwOt27dQjwexxdffIFsNss0WPLEEKOW5mln1ApyuVxoNpvMu+J0OjE/Pw+NRgOj0QiNRoO5uTnYbDa4XC64XC5UKhWWPf/FF18gEolgfX0dmUxmyCoft0voIMhQ0Gg08Hg88Hq98Pl8cLvd2N7eRjabRSgUQiQSee3qCzlA73Oz2UShUGDhaupcWy6XWWNAAEyGwWAQs7Oz7IL/7rvvEA6HmSIjzdUaFzlKrWqq6jt//jyuXr2KxcVFuFwuhMNhfPbZZ0gmk3j8+DEqlcqQ91gu0HtGiky1WkU6nYZOp4MgPJ2tNTU1BYVCgXg8js3NTTSbTVQqFXY2UdNAu92Oq1ev4sqVK3A4HKwAw2q1ot/vY2NjA/v7+/j666/x61//muWAAuN1x5EiU6vVUKlUhhQZg8GA6elpzM3NDYVwaRgkDd8kbxclDguCwNpgUJk7KYzAmxsKR7ZqKT+GMsWpHp3CShRfo++VA6OKTCaTwfb2Nur1OgKBAEv6pVkUJA9Sdij5N5lMsiZ35Gmg0j059LIAwMa7U1fNTqeDmZkZZkGSEkyuTXILd7tdxONxRKNRFItFNpk2FAohnU6z8klAPuvqRYzmnlFIpF6vP2fljIty+0OR7gepIgM8tbh9Ph/6/T7S6TQKhQKrrPD7/Th//jy8Xi/bh5FIBFtbW8jn88/9jnGCvOLUUT0QCCAQCKBer+Phw4d48uQJIpEICoWCLAeJjiJdH7lcDiaTCeVymRUNAMClS5fQ6/XQbrdZSxAKS/l8PpjNZiwuLmJqaortO+BpA8F6vY61tTXs7OywJqenPXfxZZAHK51OAwAbE0PhfcpFozAu5dNQPy+p0tbtdpHJZNBoNBAKhbC3t4dsNsvahByWfI4stEQxfL1eD4fDwToo0iKRYwyfLKBWq4VkMolms4kbN27A5XLh6tWrCAQCbN4U8LScmNpXt9ttRCIRlEolrK2t4fHjxygWi8z9Jp1z8qLmcOOANG+K3JHnz59Hr9fDtWvXhjwopLhQ7x1SDhuNBtbX19kgyY2NDdYrhhLPpL9PjlA/ELvdzubk6PV6NJtNNstlNDlR7lBFW6VSwf7+Pqanp6FUKmEymVgSJnXStlqtcDgcuHTpEv7kT/6E9RxKJBK4ffs2bt++jXK5PLZdxqUu/bm5OXg8Hly8eBGXLl3Cr371K3zxxRfY3d3F/fv3WTmw3DwxL0IQBNTrdYRCIQBAMpmESqWCz+fD3NwcZmZm8OMf/3gotEQXNY2tMJlMrCqp0+mgVCrh4cOHSCQS+PnPf47Hjx+jXq+PbUiSzt92u43NzU0kk0ns7e3B6/XC7XbD7Xaz/mivgipSyTD45ptvsLW1NdRI77BaGhzp6qXQAbUKJ1cU/X2058m4I020pX4vqVQKrVYLdrsdrVYLNpsNNpuNJaG2Wi0Ui0W0Wi1Eo1GUSiXWkIpao4/OoDrNCb0HQWuhXq+zRK/RigBgeJZHu91GPp9npYzVapX1msnlcqyFfLvdHprMK5c19TrQ8+p0OmYpqlQqtt8mKbw0WtqpVCphsVjQ7/fh9/tRLBbZgez3+9lEX2qJQE3hpO0MxpHRyhNpuICSNekMlnZulSu0R6iEOJ/PIxqNMoXEZDKh2+1Cr9ezr0nDilJlr9lsotFosBEg4XAYqVQK+XyerZ1x3XPSu6Xdbg+1xgDA8hUpWZr+j7RfHFWBUY7N3t4ecrkc84hSe43DPKcPXZGhFyft3JvJZGAwGJjVRLE3ucwvkUIHBnVU/frrr6FWq/HgwQNWX0+KTKVSYT0/Op3OUCdWSlQFXqzVj5PcaE20Wi22sP/lX/4FZrOZNUqShoMEQWBdiyuVCnZ2dtg4C5qbVCgU2IEzWt00CUgr/yg0MD8/D6fTic3NTZhMJlZ5MwnQ+05VkLu7u/j666/hcrlw6dIlnDlzBiqVCu+++y48Hg9rHuhyuZDJZFiO1fb2NkqlErNMx3k9kddTr9ejVqshnU5jf38fT548QblcZuH/SVD86b2s1+vY3t5GOp3GYDCAzWbDysoK3G43ZmZmMDMzw8JLarWaNe2kVIhkMolCoYBEIoHV1VXk83ncu3cPpVKJGWfjXsBCigndUf/yL/+C1dVVXLt2DVevXoXL5cLS0hLLqZIqJVSJHI1GcefOHaTTaXzxxRfI5/PMW0yNBA+TI51+3ev10Gq1UCgUYDKZmEZLi4Ji+XLZRFJtlp6fxpt3u11otVrWUEiqyNDQzNEhiS9LVB0nJQZ4tjFI8ahUKkxJ6Xa7EEVxKDFVEATk83mEw2Gm+FSrVdYMsF6vo1arDTVZHPdL5/tAhgIpMs1mE81mkyW3vmgSrdyhZ6W4Pa0Xyq/S6/Wsd5Hb7YbL5WJeq3q9ztobUHfsF833GkfIC0oGABUg0HqZJMjIpDM5Go2iXC6zsCxVSpIiI4oiDAYDG85LXmGqJg2FQkypoeokOYVzKReGclqouGAwGMDlcrFp39Kzhiank4ySySSrVqK8mqOY/H0kHhkaDEmj4P/X//pfMJlM7GAgjVg6pVkOb/woZPEAYJ6WRqOBTCYz1O+CPgMYilXLRSaknNBC3t3dxc9+9jPW0I3WhdQdS2W07XYb5XIZnU6HhY8o/i/92ZMCPW+1WsXW1haq1Sq+/PJLOBwO5HI5lnRISuIkMhgMkE6ncePGDfh8PlitVtbjY3FxEQDQarUQCoWwurqKdDqNR48esVbp4+7ZI49Au93GxsYGwuEwkskkTCYTtra22DDWw3bvjwMkm06ng3A4DLVajWQyOTR8lS5wlUrFDPBms8kUQvpIJpPMEH3dIZPjgtSIpqa1nU4HOzs7zxVi0PcDYIZVsVhEOBxGo9FAtVplsjmq9XZkoSVyTRWLRdy9e5dpbsBTj4zU5S2XjfSiRFOSBYVAqDGSdKFIP48qduPmfTkIUtooNESloRRylEIeKmkjJWmPC7kcGD8EQXjaqyibzQIAK7vOZrOo1WosL0tO1uH3pVKpYHd3F41GA9FoFIPBAEtLS7DZbKhWqyxX5OHDh8hms8wbI4fLfbRiRKlUIpfLQa1WDzX5G+dnfBPIM1MsFlkfKkEQEIvFYDKZhrzDWq2WKT6UMkBGKfUkkmPvHeDZ+mg0GhgMBmg0GkgkEqzSizzi0u+lXFgaaiw1To9yXx350EhKMJO+0aMTmuXI6PO9KFwkZ2/Uixh9Tpq59aLnlyp7oxthUuT1IqQhFArb3rp1i01E73Q6yGazzyVQTxKkGHc6HeTzedy4cQNGoxFOpxN6vR7tdpvJaXd3l5Wsy2l9SQ0oSvqmzs+cZ54Z+jOAIeWfLmAKQ0lHYJAHnf6/3I0q2hPSnERpAYp0v0gLfEb/7Sg58po7KjGWHqqTWlki/fwq5HoJvSiP6KDvlfsh8UOgQ4XcuLlcbsj7Mslyk+4vUmS++uqr5/5t1HsqtS7lBCkudEnL9Tl/CNIcKIogjI6keJmXfdwTer8P0jObKo7JG/Uig50+H2dH9WNpHjBJCgvn9SCL8aC1wdfNq3mRHLncnvEyg2lS5CXnZztMXkdOk+ZBfxGjz/4yWRy3jI5ckZk0zwvn9eHr4s0g+U2KZfh94fLhl+/3gd9VB3Oa1xL3MXI4HA6HwxlbBLnmYnA4HA6Hw5E/3CPD4XA4HA5nbOGKDIfD4XA4nLGFKzIcDofD4XDGFq7IcDgcDofDGVu4IsPhcDgcDmds4YoMh8PhcDicsYUrMhwOh8PhcMYWrshwOBwOh8MZW7giw+FwOBwOZ2zhigyHw+FwOJyxhSsyHA6Hw+FwxhauyHA4HA6Hwxlbjl2REQTBLgjC3wmCUBMEYV8QhL867tdwWhEE4T8JgnBbEISWIAj/46Rfz2mDy+dg+N46GL5+Xg6XzcFw+byakzx/VMf1iyT8NwBtAB4AVwD8oyAIDwaDwdoJvJbTRhzAfwXwUwC6E34tpxEun4Phe+tg+Pp5OVw2B8Pl82pO7Pw5Vo+MIAgGAH8B4P8bDAbVwWDwGwA/A/D/HOfrOK0MBoO/HQwGfw8gd9Kv5TTC5fNy+N56NXz9vBwum4Ph8jmYkz5/jju0tAygOxgMNiVfewDg/DG/Dg5HbvC9xeFwTooTPX+OW5ExAiiPfK0EwHTMr4PDkRt8b3E4nJPiRM+f41ZkqgDMI18zA6gc8+vgcOQG31scDuekONHz57gVmU0AKkEQliRfuwyAJyNyOG8G31scDuekONHz51gVmcFgUAPwtwD+iyAIBkEQPgDwpwD+53G+jtOKIAgqQRC0AJQAlIIgaAVBOInKslMJl8/L4Xvr1fD183K4bA6Gy+dgTvr8OYmGeP8RT8vX0gD+BsB/4OWhjL8G0ADwnwH8+3/981+f6Cs6XXD5HAzfWwfD18/L4bI5GC6fV3Ni548wGAyO4/dwOBwOh8PhHDp8RAGHw+FwOJyxhSsyHA6Hw+FwxhauyHA4HA6HwxlbuCLD4XA4HA5nbDmwfMxoNI5dJnC1WhWO63cZDIaxk0+tVjs2+fD1czAWi2Xs5FMqlY5NPnq9fuzkU6/Xj00+Op1u7OTTaDSORT4mk2nsZFOpVPjZfAAHnc3cI8PhcDgcDmds4Q19OJwJgtotDAYD9mdBeGroKBTcrun3+xgMBkwmgiCwP3M4wLM91O/3AYCvFQnS84U4DrlwRYbDmTBIiRk9bCadUbkIgjCk1HA4BN8/+CYAMAAAe6JJREFUL+dFsjnqfXToioxUI+v1egCevcnSh6MHk2qzwDOrcBIXhlR2/X4fCoUCCoWCLYRJRLqOaIMolUoolcoTfmXjBa0fpVIJhUIBtVoNtVqNfr+PXq+Hfr+PdruNfr8/sXtPEATodDoolUq21nq9Hrrd7km/vBODzp7RDzqbJgnpGaxWq6HRaAAA3W4Xg8EA3W73OS/NpECeTJVKBZVKxc7ofr+PVqvF5HJUHIlHpt/vo9/vP3cAjLqdSJERBIFd2PR16We5M3pY0OVCl86kIr1MSC60dqTrhXMwUgtJqVRCpVLBaDTCYDCg2+2i2Wyi2+2i0+k8F26aBKQXlE6ng0ajQa/XQ6/XQ7vdZhfVJMkEGF4DtPdoL6pUqueMTjkbW6N3l0qlgslkAgA0m022XsgAnSSkZ7NSqYRWq4VKpYJWq2XnivTsPop9dGiKjPSgFEURoihCr9c/929SxYWsnX6/zx623W6zBTEphyo9pyiKUCgUEEURarUa7XYblcrTKeiTtjmAp8+sVCphtVqhUqmgVquhUChQqVRQrVZP+uWdeqT7hyyl6elpWK1WeL1eeL1eVKtVpFIplEolPHnyBNVqVfb7jZDKR6PRQKPR4MKFC/B6vSgWiygWi8jn89jf32cX+KTIBhiWj16vh1KphFqthlKpRKPRQKPRGPq+ScBsNsNoNMLtduPMmTMAgHw+j0ajgc3NTWSz2RN+hcePQqGA3W6HVquF0+mE0+mERqOBXq9Hs9lEOBxGrVZDOp1GtVo9kn10qIrMYDCAVquFwWCAw+HA0tISFAoF2u02AECv1zOXtkqlQrPZRLFYRLvdRi6XQ7vdRjabRa1WQ7vdRqfTmYgEKnLVGgwGaDQathiSySTW1tbQ6/Wg0WhkLwdC6ro2GAxYXl6G0WiEzWaDVqvFo0ePsLq6+sKEVc4w/X4fSqUSOp0ORqMR77zzDpaXl3H16lVcvnwZ0WgUd+7cwf7+PhKJBOr1+kTJtd/vQ61Ww2w2w2az4U//9E9x/fp1bGxsYGNjA0+ePEEkEkGn04FCoZg4Zabf70MURTgcDmi1WtjtdhgMBoRCIYTD4YlQYqT7wePxYHFxERcvXsSf/dmfod/vY3d3F9lsFn/zN3+DUqnEvDOA/PfPYDCAWq3G4uIifD4fzp49izNnzkCn08FisaBcLuPWrVtIp9O4ceMGKpXK6fbIkLfFZrPB5XLB4/Fgfn6eKTL9fh8Gg4EpMaTIlMtltFotWK1WNJtN6HQ6VCoVFAoFFItF5rYC5L8o6LLx+XyYnp6GKIpIJpNotVrMYyV36NAQBIF59YLBIGw2G1Nkkskk9Ho9Op0OWq3WSb/kU4c0l0itVkOr1cLv98NisSAYDMLn80Gr1aLVaqHRaKBSqaBer7P8mEm4nIBnclKpVHA6nXC5XHC5XHA4HNDpdM9936SiUqlgt9thMpkQCARgMpnQbDaRz+fR6XTQbDYnQsGjHCq73Q673Q6bzYbBYIBqtQqlUgmXywW73T7kMZarTKRntE6ng8vlwtTUFEwmEwaDAdrtNsrlMtrtNpxOJ9RqNZxOJwqFAprNJju3D0s+b6zI0OFnNpuh0+nw7rvv4t1334Xf78eFCxegVCrRbreZ5jaaSNfpdNDtdlGtVtFut7Gzs4NcLoc7d+7g9u3bzGtDB7PcFgbJQaFQwO/3IxgM4pNPPsGHH36I9fV1WK1WpNNp3Lt3j4WZ5AwprqIowul0YnZ2Fn/5l3+J6elpGI1GqNVqiKKITCaDfD6PSCTCvA5yWxvfF7psKTSr1+vh9Xrh8XjwB3/wB/B6vVhZWYHL5UIoFMIvfvEL7O/v49tvv0WpVHourCR3edJaMxqN+PGPf4zZ2VmcP38ePp8P9+/fRzKZRLFYZDkygPxlIoXOaaPRiA8//BDBYBCXL1+Gx+PBP/3TP0GhUCCTyWB7e5vtQbkxmpPn8/lw6dIlLCwswGazQaVSwWw2o1qtIhQKwWKx4P79+3jw4AFLqpfbmqE7i85oh8OBjz76CFeuXMHu7i7W19dRrVaRzWZht9vxx3/8xzAajWg2m3A4HNjc3MTW1hYLeR8Gb/xTSDOjmJjL5cL09DQ8Hg88Hg8UCgWazebQZUMJQMDTBdLv92E2m9HpdNDpdGA0GhEOh2G1WlEul1GpVCaimkKj0UCn08HhcGB6ehqVSgV+v58pgdKkYLnLghY5xV09Hg+MRiNEUWRWc7fbhUqlYknlkyCXVyGt6tLr9XA4HPB4PJienobX64XL5YLFYkGv10Mmk0E6nUYqlUK1WmVynASvDIUvycr2eDzw+XxsjfX7fTQaDbRarYn1yFAuo0ajgcvlgs/nQyAQgM/ng8vlgtlsZsbVJJxLgiBAq9XCarXCaDSyvD2q0rHb7cybJ+c1I61cMxqNMJvNsFqtsFgsGAwGyOfzKBaLiMfj6Pf7TGZOpxNutxuxWOzQX9MbKTLSDO65uTlMTU3hypUruHz5MvOutFot5HI5dDodlEolNJtNFAoFFAoFmM1m+P1+GAwGTE1NQafTYWZmBvPz89BqtfD5fNjd3cXXX3+Ner2OUqmEbrcr28TXZrOJer3OtF2v14v33nsPoVAIjx49YqEA6YUjR0jZrVQqLNlSEATMz89DFEVcuHABAPDkyRMolUqUSiUWgqP/P0mMVlQEAgHMzMxgcXERn3zyCWw2GxYXF2EwGKBSqdDv91GtVpHP55HJZJBIJFjlxSSEcekgtlgs8Pl8zBMzNTUFpVKJYrGITCaDVCqFcrks2wvpZZB8NBoNzGYzvF4vFhcXMT8/z5I6DQYDzGYzCoXCSb/cY0MQBJhMJvh8PmYMUBUgeWYol0iuhjcpqyqVChaLBdevX4fL5UKj0cD6+jpu3ryJGzduoNlsolqtolKpIBQKodPpIBgMwmKxIJ/P48mTJ6y6GXjz8+ZQ/DqCIMDhcCAQCMDv98Pn8yGVSmFvb49VRTQaDaRSKVQqFSQSCSSTSZb5bbfbYbFYoFAo2CJpNpvs56+urkIQBFQqFVlr/t1ul9XcKxQKmEwmzM7OotvtskRpshDl+PzAswVN/Qfq9TqKxSIKhQJTVNxuNy5fvgwAWFtbg1KpRCaTObHXfBqQXrZ2ux2zs7NYWVnBj370I2Y1KRQK1Ot1VlJMinO5XEan04FKpZJ9cr1UThqNBh6Ph1VwuVwudLtd1Ot1NBoN1Gq1icn/kEIhN6VSyRQWKkDQ6/XMEyGKoizDSS+C9oVWq2VpFMCz1AqlUsmiEmq1WtbKr1QWwWAQXq8X3W4X6XQakUgE29vbrCJZp9Mhl8vBZDKxhHq73Q61Ws0qlg9jf/1gRUaqmZH3ZH5+HiqVCslkEhsbG/j8889RKpWQSqXYodnpdFCtVlGr1VAul5HP51kWvMViwQcffICZmRkolUqcPXsWABCJRJBKpViikJwg9+1gMEAmk2EerFarBZVKBY/Hg3K5DKPRCK1WO1RVIufDlUrxSbOn0AdZRUqlElNTU1hZWUE8Hkc4HGaXzqTkM4yWV3s8HpjNZrzzzjt49913YbfbmTK4traGbrcLs9kMjUYDq9WKd999F1qtlpVd12o1WVdbSC8XKhk9f/48pqenYbPZoNPpEIlEUCwWkU6nUSwWUa/XZSmLg6CLymq14uLFi5idnYXT6YTBYEC73Uar1WLePDIu5Yg0hGKz2VjqxMuSwaX9VOS4ZqTJ8TqdDiaTiSU4r66uIplMIplMDrVb6Xa7iEQiAIDl5WVYLBaYTCYYjUY0Gg2WP/umvLFHRqVSQaPRwOv1Ym5uDmq1Gul0GhsbG/jlL3+JQqGAZDKJdrvNGrzRRqHGb2q1Gg8fPmSHbLvdxrlz53DmzBkIgoBYLIb9/X3cu3dPlpc4PUs2m0WpVEI+n0er1YJGo4Hb7UapVILBYIBWq2VKj9wZDAbodDpot9uo1Wqo1WospGYwGGA0GlGtVrGysgKtVotvv/2W5VvJ1a0rRboPKKlwamoKgUAAb731Fj799FNWFVgoFHDz5k3UajUsLy8z63phYQHdbhe//vWvoVar0Wg00Ol0ZGtlSxM3lUolbDYbS+61Wq3QarUoFouIRqPIZDIolUpot9uy91JJkcrIbrfjwoULCAaDrOy6VCqhXq8jn88jnU7LPvRGz2a1WuFwOOB2u2G321l6w+izy10WlIOn0+lgNpuZYpdOp7G2toZMJsPOX4VCwRSZbreLpaUlWCwW1otnMBgcWpTlBykyUs3MYrHAZrPB4XCw0rN0Oo18Po9qtco8KNJ2+9KmePTGN5tNKJVK7O/vs74OPp8P/X4fLpcL9XodFotlyDUutwPmRc8i56Sxl0HJptQdulgswmQyoV6vo9VqsQS7SYbkQ95Qk8mES5cuYWZmBh6PB81mE+l0Gk+ePEE+n8fq6iprYFYqlaBSqeD1emE0GjE1NQWNRoNsNst6PsmVwWAAvV7PrEm32w2bzcaSe1OpFPb395HNZtFqtdDr9SZm5hKdNdTQ1GKxsKoU6mNFinEul2MeK0C+AxMpxOb1eplnSnr2SL0x0gavcoU8VFqtFhqNhoWjpfmbox2hG43G0L3d7XaH8vEOgx98G/T7fahUKgQCAXg8HszOzmJmZgb379/HxsYGQqEQ63gonY0zutipHJvi0Tdv3sT29jY7aAeDARYXFyGKIoLBILrdLjKZDNrttuza1FOsdXQhkLtSzhuEkFbM9Pt9NJtNxONxDAYDFAoF1Ot1FqcHJk/Rkyb2UqPEa9euIRAI4I/+6I+wsrKCSqWCUqmE9fV1/O///b+RzWaxubmJTqeDZDIJv98Po9GIixcvwu124+rVq4hGo9jb22MXk9wubqnb32azYW5uDouLi1heXobBYECv10OlUsHjx49x7949lt9He1KuBQZSaC9RMzOv14vl5WW4XC4YDAYoFAqm6IVCIcTjcaboyWmtEIPB0/lJoiji7NmzuH79Ois4II8xABZZaLfbrDO9XKEKWgoRUbf1ZrPJPJhSb1Wv10OxWIRarUa1WmXhpNG5VCea7KtQKKDX61lHWrVaDQCsN8xoW+9XvVhSaBQKBdP6yYVFCYsWi2XiMuUnEamVU6/XUavV0Gq10G63J6rL8Ysgb6her4fdbmfJqnq9HgqFAtVqFYlEAqlUCtlsFsVikc1TKpfL0Ov1rG8Txf+r1SobjUF9U+SmzADP2u3bbDZYLBbodDqIosguolqthmKxOJRULzcZvAq9Xs88MWazGQaDgRlUlNdYq9WGZnPJFYocUKUWeSCkxhZ5YihBXO4d6UVRZCXovV4PrVaLNbmTejAJaSqJ1IN1mGvnB4eWyCPj8Xjg9/thMpkgiiIEQWBuo9d9I6WNcXK5HHOFOxwOLCws4OOPP4YgCFhZWWFZ0Pl8XrYLRcpoKG4SkIaWWq0WEokEOp0OSy4kRWYS3v9R6EJxOp04e/YsgsEgfvd3fxcejweCICAajeK7777DzZs3mZeFqr1UKhUymQzq9TrC4TASiQQA4Ny5czAajSwkRd225SLj0eRvr9eLy5cvY3FxERaLBYIgsJyYRCKBSCSCcrk8sU0WZ2dn8e677+LcuXNYWFiARqNBp9NBrVbD5uYmHj58iGg0yi5sOeZUkRIriiLr70UGO/BMwen3+6jX66hWq4hEItjc3EQ+n5e1F89ut+P69euwWCysRUYul0OhUGChJzJCSQmkWV2k+J2a0BIA9kZrtdqhgZA/9GcBYOMMyDJqNpuszToleU5SfoRUi52EHh+j9Hq9oVJY8iwQcrcIR6FwqsFgYJ4Yh8MBi8WCbDbL2h1Qwupo3yFKXm02m2g0GhBFEQaDYWh8iFxzQuiMMhgMsNlszDVOa4wqt14U658ESCmhoYh2ux06nQ4qlQqVSoWFDyhlQK57T/pcVGpOHy9STqQeGWl1pVzXj0ajgcPhgF6vZ89NY3QoF5YQhGcDWcnJMarIHIacDqWzr/SyfVNI0yWXFTXAozJvqdIkV0gGNEuoVCoxWUxCngwwbPHQQNFIJMIacnm9XgDPrz85XsAAWJjW6XTCbrfjypUr+PM//3PW06JSqeDGjRvY3NxkLcAphk95aKTIUIipWCxCp9OxtUYxfjnJkNaHQqGAw+GA0WjEwsICzp49C6fTyfpT3bp1C5FIBPv7+2wkilxk8DKknk8qt9bpdFhaWsLly5fhdruhVCrR6XSQy+WQzWaxtbWFtbU12XuspC34qZjFarVCo9EMXcDkeWi32yw3rdlsDhW2yAlBEGCxWLCyssKKc8iDS53W6bwBns4PPHv2LOtQn0gk2HiZw9xjh6LIHCb0YJRMJU0mI61YbotjFHq+Xq/HLGfKO5oUpFVttVoNAFAoFJg1SEg9VXK1EIFnOUN6vR5utxvT09O4ePEiRFFEpVJBrVbD9vY27t+/z/JjaGikdL/QGqJ1RUYDWZWkMAHy8fzR8xgMhqFW6UajEQDQarUQDocRCoVYryppgYLckU5IpzJjSggHwPLUKpUKstks6xUi19CJ1CDS6/UsT0ir1UKlUj13zlAlDvVskvs5rdVq4Xa72eRv8pJLjWw6O9RqNdxuN7xeLzqdDjur6OyhdJQ35dTFaOihyuUyYrEYpqam2ORnOV9Uo1AmOMUeSZkh60kul8zr0O12WSMu2jSU8EoJ4FQOSFUDcpEPHQ4qlQoKhQKzs7P40Y9+xCr5Go0GvvvuOySTSWxtbSGZTKJer7PvH5UD7SM6RMjtSx9SxWfcvRJ0XlBo+uzZs1hYWMDS0hJsNhuAp72bEokEdnd3EQqFUKvVJmZ/kXxUKhVEUcTc3Bymp6cxOzsLq9UKlUrFGnR+9913iMViSKVSsg2djDaYNBqNrM8Q9dIhjwxFCVqtFqLRKFKpFOLxOLLZLLuk5SYfQhp9GQ0lSb+H/p0KdSqVylDDzcOUz6lVZKrVKuLxOPL5/FDd+aQoMwqFAu12myU2U9naJDR7IyjTnZq0UdXSqCJjNpuh1WohiiJbK3JAemBQrH5mZgY/+tGP4HQ6odFokMvlcPPmTezt7WFzcxPJZJJ974ugZGEaBqjRaFjIlv5OnrDTvM5e5TUi2VGzQHJxX7t2DUtLS7Db7SiXy0ilUkgmk9jd3cX+/j5TZCYBeo+pvf78/DzOnj2LmZkZWK1WdDod1Ot15HI53Lp1C3t7e0in02zGkBybc9KaoQv47NmzmJ6eRiAQgN1uZ94XUnZ6vR4ikQii0SgLm8g50ZeQ7j9pj7jR7yE5mkwmluN4FEreqVNk6ACigWWU7fwyYckN6QKhqp3RgX6TiFSDp8uYcmUajQYbYEfKDv0fOaBUKuH3+2G32zEzM8PcuhsbG4jH48xSpu7ZLzpQ6NIiq9LtdsNqtWIwGCCbzbJEYZr1dRplR3uDQmb0Ner3IX3d0spKh8MBm80Gr9cLj8cDnU6HTqeDYrGInZ0d7O/vo1KpoNFoTIzHk+SjVqtZmfX09DTm5uZgtVohCAIajQZisRii0SiSySRrmCiVsVyR5mVK+1aR4tdsNpHNZlkX+1gshnK5LPt1Azz1kNdqNSiVSoiiCJ1OB7Vazc4eadM8as9C97gUWXtkaIMZjUZ2eEuFJOeFIrXABeHpBOhqtco62pInQs4yeBEvKkHv9XowGo04d+4cG5FRqVRY5Ymc8hvUajWuXr2KlZUVvPPOO1heXsaTJ0/w+eefIxaL4eHDh8jlcuziliK96Mmj4/P5sLy8jGAwiFQqhUePHiESiaBQKDD372nda+SJM5lMEASBhVtpajzwLAmThq3Oz8/D7/fj7NmzWFlZgSA87UQai8Xw1VdfIR6PI5lMsotoUgwmGuo3NzcHn8+H69ev4+rVq9DpdBAEAfl8Hvfu3cP+/j4LW5I3Ro7yGQ2ZUId5i8XCcmPIoE6n03j48CHC4TB++ctfIpFIIJfLyX7tDAYDNJtN5PN5psRQaJ/Onn6/D41GA6PRCIvFArvdzsYYEKfOI/Mi6+9Ff/6+P1OtVkOv17NmVTStl6wmOUKHKF3C1N6Z8kLkmIj5JtAIA2lFl1ysRFJYqX+F0+mEz+eDRqNhE8ETiQTS6fRQuTBd4tIOwAqFgiUrer1eTE1NsWnzrVYL2WyWDSodbWJ5GhltqjVaMUmvnyxqp9MJl8sFo9EIjUbDmneVy2Vks1kUCgXW3O00P/dhQp4FrVYLl8vFBo7SMMRGo4FyuczWmDQ3Te5oNBrYbDZWok8tP6QNEulslpbtyyWk/Tp0Oh1WoUQtG6RnD01Op2ngZDxUKpUjGYNyKIqMtNSMytFGS7JfdUCMxtwsFgvLnK9UKsjlctjf32dxbDluKEq6o+qsZrOJWCyGeDzO2jvT4plU6GJWKpUolUoIhULY3d1FKpVCPp9nvQzGGfJKarVazMzMwOl04r333sP169eRyWRw9+5d3LlzB1999RXK5TKr6pL+f+n+0Gg0WF5ehsPhwE9/+lOcOXMGVquVjX/4+uuvWUM4kt9pvdCpFwWNUpCO76DzBwAzhAKBAN577z3MzMzA7/dDq9UinU4jEolgY2MDDx48eK4B4Gl99sOA1oZKpWK9iD788EPMzMxgamoKOp0OmUwG2WwWjx49wi9/+Uvk83mUSiXW4Eyu8qHn8nq9eP/99xEMBnHhwgW43W7odLqh3KBOp4NyuYxSqYRSqYRyuXxqQ7KHDVXTqlQqNjSaoiZkUOr1ekxPTyMYDMJgMLBS7bW1NaRSqSGl8DA49D4yUve/9IW+rrVDAqGyN1EU0Ww2Ua/XUS6XUalUZFfeJu0iqdVqmQb7Io/MJDUDPAgKK9CaaDabLBma/n1ckSaqknvWarXCarUinU4zD0o+n2chFen+os+k8BkMBjYg0ePxwOVyAQBqtRpKpRIbAHjaLW6pokLW74v6V9FFTdUS1G5fq9VCEJ42AywUCiyUVq1WJ8qaJotZp9PBaDTC4XDA6XRCq9WyIoNSqYRCocAmXMvtzJUiDScJwrNmkx6PByaTaagrrTTFYTQfa1KgHMWX5ZNJw78mk4kZ3uRJbjabp6dqiSyjfD4PrVaLRqPB8hb8fj/S6TQMBsNQHPugCgOK7585cwYulwsffPABPvzwQ9TrdWxubiISieDx48eIxWKo1WrMnTXukAdLoVDg/PnzmJubw/z8PNs45L6cpI3yKkhZ7nQ67FInj5VcrKJerwe1Wo2pqSkEAgEYDAYMBgNEo1HcunUL29vbqNVqQwm+ZFBQBZLD4cDs7Cy8Xi9+//d/Hy6XiyX7Pnz4EKurq9je3kYoFGIJ5afdmyWtZJNCihy1QHc4HHjvvfcwNTWFlZUVeL1eaDQatFot7Ozs4MaNG9jZ2UGxWGSubjl7GwhS/CwWC86dO4fZ2VnMz88jGAyyUQSRSAS3b9/G48ePWViJFGs5yodkQvkwZ86cwUcffQS73c7K0Ee/12q14uLFi8xIoO7jlAwtRzkRlF9FAzWl4V0K6TqdTjbMVq/Xo9frIZFIYGtrC4VC4dDzrH6QIjMaJ6RBWf1+n41/NxqNEEUR7XabHTov8spIrSmFQgGXy4XZ2Vn2EY1GsbGxgWQyiVQqJcvyNrp8vV4vFhcXYbPZWGkjye80W8rHDa2/Xq+HWq3GRsS/SmEeF6QeGavVyhLe+/0+SqUSIpEI63ZM7m7p/6NyWqvVivn5eQQCAVy5cgVOp5MN38xkMnj06BFisRgKhcKQ23wcGFXspdbxYDCAwWDA7OwspqamWLv9TqfDlN+dnR0kEgmmwMnFMHpdtFotfD7f0IgLao5YLBYRiUSQSqVQrVbR6XQgiqIsy62B4UpZi8UCp9OJubk5mM1maDQaqFSq58aiUL5ZtVpldx2NbZD7OiLjW5qzKVVkyKsVCATg8XhYp99SqcT67Jwqj0y322UNktLpNPL5PHQ6HRYXF1Eul7G8vIxcLoft7W3U63WW8T368KIowuv1wmw245133sHZs2fh8/nYFN9Hjx4hHo8zl5RcoDee5kj5/X7Mzc3BaDSiVquxSbMUf5WrRfQqDkrslJP1Iy0vptBIIBBAMBgEADbnJpPJoFqtsnwpnU4HpVIJk8kEjUaD6elp5sm5fPkydDod64r85MkTVnGxs7ODSqUCYPxCcaOvlxQb8kaRUUCemH6/z8Ik4XAY0WgUxWJxKBQ+CdB+MZvNWFxcZN4+pVLJhrJubm5ibW0N6XSaJQVLw3pyQnqmGAwG+Hw+2O12iKLIPDHS+yqRSLAE8XA4zJrgSdsWjNte+r7Q0Fm73Y75+fmh+WzUk8putyMQCMBkMiGdTqNWq6FQKAxNBz9MfrBHhrT3VCrFLLx8Pg+Hw4FgMIhSqYSlpSXo9Xrs7e0NxbTpszSp0e/3w+Px4Pr167h27RparRZqtRpSqRQePnyIfD6PZrN5eE9+CpAqMkajET6fD7Ozs9Dr9ajVamyyaKlUYhb6pPIiRWb00JB6Csf1MJF6VMxmMwKBAAKBAIBnigxZNdS9l5oBer1eWK1WXL58GZcvX4bP58OlS5fQaDRw//59ZDIZ/OpXv8Lm5iYymQwymcxz+QHjCq0LrVYLi8UCr9eLhYUFOBwOpshkMhkkk0nWwIw8xeP+7N8XQRBgMpmwsLAAr9cLvV4PhULBuhxvbW3h8ePHLAFa7sUFdA8ZjUbmoaIEVuDZnLN+v49EIoHHjx+zz4VCge1HMjblTrVaRTQaRbPZxOzs7FCYiLxaLpcLfr8fSqUSoVAI2WyW5fQBp6z8ejB4Og+p0WggmUwiFApBqVSyBkuLi4vQ6/UIh8PIZrMsaZUuHGqoY7FYcPbsWZZcRdZTNBrFzs4Ou8zH+YIaRRo+0Ol00Ov10Ol0rLMqucGlM5bk8uyvCyl6arWadaCl5kuE3CwgemYK89DzqdVqiKIIv9+PS5cusYGiSqWSDbNzOp0wGAyYmpqCzWZDv9/H1tYWSqUS7ty5g2w2i0gkgmKxKKvpxVJ3PjUNDAQCsFqtbEJvt9tFNBrF7u4u0un0UGL4JEHKr16vZ4msdMaEw2Hs7u4imUwOTZmX0/6SIvWAKpVKWCwW+Hw+OBwOiKI41AaDjHcaG0P3U6VSYd6YSWiNIQhPB89mMhnmEdbr9QgGg1hYWIDP54PH48Hy8jLMZjMajQbzXEkjC4fNG4WWKEdmMBjg8ePHaLfb0Gg0mJubg9vtxqeffopEIoFqtcosoXK5zDaT0WiE0+mEx+PBH/zBH8DtdsPpdKLX6+HJkyf48ssvsb+/j729PeaOkovGSxo+5UG4XC5YrVamyFE7Z/o47HK1045U0aND12q1wmazsYZdciuXlR6EoiiyvASSgV6vx5UrV+BwOFjDLlEUEQgEmFdGqVSyvkvRaBS/+tWvkEgk8PnnnyOXy7GZXVK5jbP8Rts2zMzM4MMPP8SZM2fg9/uhUqlQrVZRLpdx9+5d3Lt3D7u7u6jX60NhE7lDcqImZlarFT6fDyaTCfV6HbVaDbdu3cLdu3ext7eHUqk01NNKbkg9u2q1GiqVCoFAABcuXEAwGGSVSqPtCMrlMvb397G7u4v79++z8SlyO4sOolqtYnt7m3WGttvtuHLlCmw2G5aWlrC8vAyPxwOv14twOIzbt29ja2sL6XT6yMJvh1LLSwli6XQamUyGdRnV6/WwWq2Ynp5mjXFKpRKrP6dJvna7nXUHLBQKyGQyrO16qVRitely3VRkEdCl1ev10Ol00G630Ww2JzbZVxp6o8ZwRqMRarWaJZxJPVbjbhFRnJmqAlqtForFIlPiqCWBx+NhLdSlc5WoKReV7EejUUSjUaTTaVQqFTaZV5pvNa6yAoYvI+q/ZLVa2fgFqqioVqsolUooFotsAOskIq0qoV5V1BSRGuDR9G/6frnyopyfZrPJ+jKRsk8pEZ1OB71eD9VqlU1wprNnUkKT0tA9TfquVCowGo1s3AnlGCmVSqTTaSSTSeRyOdaj6qjk9EaKDC2GXq+Hra0txGIxAECr1UIgEMClS5dgMpngdrvR6XSQTCZRqVTYJtLr9WwKbbfbRaPRwOeff44nT55gb28Pu7u77JKSs+VEC4QOGOqZk8/nkUwmUa1Wh3oYTAK0rkRRhMvlgtPpxMLCAmvmRusln8+jWCwyZW+cD1/yEHS7XZaI+s033yAUCjElxuv1YmZmhsmnWq1ic3MTpVIJT548QTabRbFYZI26YrEYWq0WC81Kp9XKYS1RyThV3pw/fx7vv/8+M4wqlQq2t7cRj8fx5MkTbG5uMisakIcMXgfaFyqVCjqdjn0olUrWXj8ajSIWi7HCDLnLRlrp1uv1sLe3hy+//BJXrlxh+Z1kWMZiMZRKJWxtbSEUCiGfz7Mk10mQFaFQKNDtdlGpVJBOp3H37l0EAgFcv34d09PTzLja29vDZ599hlgsho2NDWQymSMbGAkckkdmMBiwGvpMJoNoNMoa2Wm1WjaETKlUol6vs6FvlORKSk6tVkMikcD+/v7Q7JNxtxxfBTUXopK2ZrPJtH4agjjOF/SbQOuGNghVElCOCHkf5DaegDwy2WwWSqUShUKB7QdRFNl4hna7jXw+j1wuh1gshmQyiWKxiGKxiGq1inw+P9RpW657iYwAitv3+33miaHE5nK5fGTJhuOC9P2nNUSjXyiMLc1jnBTIc5dKpZBOp9kAWgrVUqUS7atWqwUAskl1eB1e5DHOZrPMQ67VatHr9dBut1EsFhEKhZBKpYbyiI5qXb2xIkNvJPXxWF1dRTKZhMvlwr1792Cz2XD16lVYLBaYTCZotVrmmiqVStjc3EShUMCdO3eQyWRw7949JBIJtFotWVmOo5Am3+/3Wd5QJBKB0+nExsYGnjx5gp2dnf+/vTPtkevM7vv/1nLr3rq170tvZDfZpEiJFKWRMjNWNB54HNgwPAGMvBkg+QT5AHlpBPkKeR8gQPzOyXgcZJIg41iWMpaGw0VcmmQv7K6la9/3PS/oc3S7RDZJiVXNuvX8gAKlZrO77qnzPM95znqiUZkR5fAiyIChTaTf7+Px48eQZRk+nw8+nw97e3vY2dlBoVA4UTWw6HIi79xgMMDu7i5SqRRqtRp3p6U1RLlUiUQCnU4HhUKBXd7UU0ff/8KIhxN5mOr1OgaDAX7zm98gkUiwYdPtdnmsyfHxsaGNudOg56V9N5/P4+DgABaLhV3/Rpsa/zL0Bp0kSchms1yFlM1mT/TOoYqbVCqF4+NjNvb0P2cZ0Hux6vU6/vEf/xEOhwOHh4cIBoN8Gc/lctjd3UWn0+FBtPp//6Z5Y/3uqb055chkMhkUCgWEw2GOW6+vr/MmTO3lk8kk8vk8bt++jUKhgGQyyXk0Rs2JIehD7ff7aLfbnB90eHjI/T6WdeI18M0NgGYJ5XI5eL1eDiMVi0Xkcjke+meUPg70DGTEmUwmNBoNLgmlAXZkqFB3Wv1tmgw6I4dQ9Mn/NPByb2+PZ7kA4Anyw+HwWzOplglaS+SBaTabKBaLkGWZu2Iv05iGacbjMRqNBiqVCprNJqrV6olQPjXdJK/VMiWKP4/JZIJer4dUKsWl+5qmcaEBhf31bUNmKas3ZshMuyw7nQ7y+TyazSYAQNM0jlvTbbLT6aBWq6HdbuPo6IiTEZehy6besqWqpH/4h3/AkydPkM1mOdRmpEqt10H/+Q+HQ7Tbbfz+979HIpGApmnQNA25XA7pdJpDS0bcWMhgabfb7KUkfaC/ow6/lAA8fVM0mkymIaOPwknk9ic3OBm5RjF0vyuUqDkYDPD06VP88pe/hNlsRr/fR6/XY0/DMspJb5iQl1wvA9KhZfSOPw/9+aVfdxTC1nus9N8/s/dzWl6Bw+H4TkkH+mZ3J37Zcx5mutLk+ypJs9mcm4ZpmvbGkjJe1LWW5PGm8j9ardbc5PNd9WcaevbpPJhZlGDPU3/cbvdL5fO8Z3/emqH/n/WGUavV5iYfu93+yvqjl5N+35nWkVnLp91uz00+qqp+5/Wl35+n9WiWdDqducjH6XR+L9lMe6fe1Pl0Go1Gw1B785uW1Wl788xGKb/qprEst8aXoTdWpmVilCTW78vzdGRZ9OZlt5tlu0G/iOd5MIVcno+Qy4uZlo2Q1fOhc+t5+9M8ZTYTQ4YewOg5Lm+aZQwhvQrLbOwu87O/DkJOr4cwfF+MPrdM8HLeBl0SJ6dAIBAIBIKF5dQcGYFAIBAIBIK3GeGREQgEAoFAsLAIQ0YgEAgEAsHCIgwZgUAgEAgEC4swZAQCgUAgECwswpARCAQCgUCwsAhDRiAQCAQCwcIiDBmBQCAQCAQLizBkBAKBQCAQLCzCkBEIBAKBQLCwCENGIBAIBALBwiIMGYFAIBAIBAvLXA0ZSZL+rSRJNyVJ6kmS9J/m+bsXAUmSfJIk/VdJklqSJB1JkvSLs35PbxNCf05HyOd0xPp6MUJ3TkfI53TOWj6WOf++YwD/AcC/AKDO+XcvAv8RQB9AGMB1AP9dkqS7k8nkwZm+q7cHoT+nI+RzOmJ9vRihO6cj5HM6ZyqfuRoyk8nkrwFAkqQPAazM83e/7UiSpAH4CwBXJ5NJE8DnkiT9DYB/DeDfnembe0sQ+nM6Qj4vRqyv0xG6czpCPqdz1vIROTJvDxcBDCeTyRPd1+4CuHJG70cgMBJifQkEBkUYMm8PDgD1qa/VADjP4L0IBEZDrC+BwKAIQ+btoQnANfU1F4DGGbwXgcBoiPUlEBgUYci8PTwBYJEk6YLua9cAiEREgeD7I9aXQGBQ5l1+bZEkSQFgBmCWJEmRJGnelVNvJZPJpAXgrwH8e0mSNEmSfgzg5wD+89m+s7cHoT+nI+TzYsT6Oh2hO6cj5HM6Zy6fyWQytxeAvwQwmXr95Tzfw9v8AuAD8N8AtAAkAPzirN/T2/QS+iPk8z3lI9bXi2UjdEfIZ2HlI/3TmxAIBAKBQCBYOESOjEAgEAgEgoVFGDICgUAgEAgWFmHICAQCgUAgWFiEISMQCAQCgWBhEYaMQCAQCASCheXUOm+3271wJU21Wk2a1++y2+0LJ592uz03+WiatnDyabVac5OPw+FYOPk0m825ycfpdC6cfBqNhtCfU5iX/oi953SMpjtn0tDneSXfkjS3z1AgEAgEAoFBmKshQwYMN7GZMl6W1Zh5kVwkSVpamQgE82CqqdcJaO2ZTCICv+w8T09IL8QeffacWYtlUgihBM+YXiiSJD3X2BMIBG+G53Qn/dbfm0wmsQ4F3+4kK/bnt4q5GTL6zcJqtcJqtWIymWA4HGIymWA0Gi2VcTOZTDAejyFJEiwWC0wmE2w2GywWCwaDAYbDIcbjMYbD4Vm/1TODdGY0GgEAzGaz8FK9BvqNdzwen9h4TSbT0t0opw8js9kMm80Gk8kEWZZP6NZ4POb11+/3n2voLAPT+mMymVhOy6I3ACDLMsxmMywWCywWC0ajEevFYDBYWqOGDDpaL6Qvej2Zh0dz7h6ZyWQCWZahaRqGwyF6vR5GoxEbMsuiDLRJms1mXiAulwuKoqDdbqPb7aLf77OhtyxyIfSbJ8kAeGbMAMtz+H5fSI76zZaM52U7jEifaNMlQ8ZqtULTNFgsz7ZDk8nEa6/X62EwGAB4fm6fkdEbMaQ/ZrMZJpOJLxVGhw5lWZYhyzJUVYWqquh2u2i1WhgOh3zpBJZ3X9Kf4ePxGCaT6cQeM2u5zNyQ0bvi3G43FEXB2toa1tbW0O12Ua1W0Ww2sb+/j3a7/a3wihGRJAk2mw0ulwuqqmJ1dRWapmFlZQUulwu1Wg21Wg2lUgkHBwfo9XpoNpsYjUZs6RoZ/W3ZYrHAbrfDZDKh0Wig3+/zQWR0OXxXaP3QBmyxWOBwOGA2m3nT7Xa76Ha7LwyrGAl6PpvNBrPZDFVVoSgK3G43otEoVFVFOByG1Wrl22Ov10O320Uul8POzg663S7q9TqGw+HS5MzoL1mapkGSJPT7fQwGA34ZHUmSYDabsb6+jkAggHA4jFAoxHrRbDbR7XYxGAyWYm+ehowWr9d7wms1HA7R6XQwGo3Q6/XY0JsVczFkyPMQjUYRjUbx6aef4tNPP0WlUsHR0RFSqRSq1SovDiMfUuRqczqdiEajCAaD+MM//EOEw2Fcu3YN0WgUmUwG2WwW9+/fxy9/+UtUKhU0m00MBgNYrVb2ShgROlitVivcbjccDgcuXLgAm82G+/fvI5fLod/vo9fr8W1JcBKSoSzLcLvdcDqduHr1KlRVRaPRQLfbRSqVQiqVOuEiN+Kao9CkJElwOp3QNA3xeByxWAzxeBzXr1+H1+vFpUuX+LCWJAn1eh2NRgO3b9/GX/3VX6FUKqHVaqHf7xvek6Vfg06nE06nE9vb21AUBcfHx3zJKpVKAIx54dRfpmVZxkcffYTr16/j6tWruHr1KutFOp1GNpvFYDCALMtn/K7nDxkyq6urCIfDcLlccLlcfLa3Wi2k02n0er2Z6snMDBm9IlitVsiyjGAwiHg8jnA4DL/fD7PZjE6ng263C0VRYLVaT4QRjLRA6JlkWYbNZoPX68Xa2hpCoRBisRiCwSC8Xi80TYPX68V4PEY4HEYsFoPVakUulztR3WQk2UxDHhlN0+DxeLCxsQFFUZDL5fhm3O12DS2D7wsZMl6vF16vF+vr63A4HKjX6+h0Ouj1eqhUKuh2u2wUAsZbc5Ik8d5C+04sFsPKygpCoRCCwSAcDgffJulWbbfbYTab4ff7EY/HYbFYkE6nMRqNvpVvZFTIK+pwONhrPJlMYLPZ0O122ZAxOiaTCQ6HAy6XC5qmsWdvOBxy/t4yoo+2eL1eXk9+vx/FYhH9fh+1Wg2FQgH9fn+m72Wmhsx4PIbVaoXX64XH48Gnn36Ka9euYWNjA5FIBF6vF36/H06nE4FAALVajb0yRtskaPMjd/bVq1fx85//nA8ZVVVhtVoBAH6/H36/H6qqAgBbtLlcDsViEa1Wizddo0F6o2kazp07h42NDfziF7+Ax+OB0+nEvXv38PDhQ1Sr1bN+q28dZIyMx2OMRiN4PB58+OGHWF1dxc9//nP4/X42ZP7X//pfGA6HKBQKaDQanDNiFMgTQ6Fsr9eLP/7jP8bFixcRDocRDodhNpv58pTNZjEajWCxWGA2m/lmubW1hT/7sz9DMplErVbD8fExyuUy2u32iYRpI0FrUJZlhMNhbGxs4Oc//zmCwSDu3LmDdDqNzz77DKlUyvCpAJQP5HK5EAwGMR6Pkc1mkU6ncXBwgFKphNFotDQ5Q9OQt/Odd97BBx98gHPnzmFzcxPHx8e4e/cukskkMpkMhyTp+9+0rGZiyOhj7mazGQ6HAx6PB6FQCNFoFG63m6sEJpMJ34goOciIcXv68Ox2O3w+H0KhEOfEUKLhcDjEYDCAoihQVZXDT6PRCIFAAP1+H81mE71e76wfZ+aQJ09RFPh8Pvj9fgSDQQSDQQ4BAMb3Tn0XKK6vqioCgQCvu0AgAE3T0Gq14PF4OAfJqFCOkM/nQyAQwMrKCtbW1hAMBhEIBDAYDDi/gbxTFLqlW7jNZkMkEsFgMIDH40Gz2US9Xj/rR5sptP9SbozL5UIoFEIkEkEqlUKn04GiKLxXGw195Q0ZMjabDTabDcCz/KlOp8PRhGXdg/TntN1uZ69MJBLBaDRCNBrlaAudb7Pije9ieiWwWCzw+Xz45JNPEIvFcOXKFayurkKSJDQaDQDGb/pG+T4ejweqquLjjz/GP//n/xzxeBzRaBT9fh+///3v0W632WtDydBmsxkXLlxAKBTCaDRCoVDA559/jsPDQ5TLZZTLZQDGathFOUSNRgM7Oztot9u4desWIpEIgsEgfvCDH6BQKODhw4cYjUZLW9U1jb6s2O12w+12Y3t7Gzdu3EAoFIIsyxgOh2g2m2g0GqhWq6hUKpxgbxTIm6CqKlwuF2KxGP78z/8c8Xgcly9fRigUgsViwXg8RqlUwuPHj1EoFPDZZ5+hUqlwUuIPf/hDfPzxxxzadDqd+PjjjxGLxdDr9dBqtfj3GU33SIZOpxOXLl3CxsYGPB4PFEWBzWaDLMuwWq28dxtJf4jxeMznl8/ng9frhdvtxmQyYSNm2pAxmh68DP3zjkYjvhi0Wi0oioLNzU2YTCY2aAqFAieIL4RHhjCbzbDb7RwiCIfDcLvdaLfb6HQ63yrRMhr6UjRVVeF2u7G2toZ3330XHo8HDocD1WoVqVQKpVKJFwTdIlVVhd/vh6ZpeOedd1AqlXB0dMSZ8qVSyZBykyQJvV4PhUIBNpsNqVQKAODxeHhjoTDcMvfZmYYOFFVV4fP5OCfN6/XCbDZzYm+32+WN2Gg9UvTeBAoHvPPOO1hfX0coFIKmadynqdPpIJPJIJVK4datW8jn81wVR3khtBbNZjNfwqY9gkYzZvShJfJeqar6rTwiIz2zHv1narfb4XA4YLfboSgKV7MNBgP0ej3ef4wqi1eFwtlUjk75ZfV6HS6XCw6HA+VyeWZrZSYeGVoETqcTfr8fW1tbWFtbQ6PRwO7uLt+irVYrV1KQUhjNuiU3fyQSQTwex/r6OoeLMpkMMpkMfve733Hm+2g0wtHREXZ3dxGLxfDee+/BarUiEonA5/Phxz/+MTY2NnD79m2YzWa0Wi02gozimZEkiUuEG40GGzLBYBChUAh+vx9erxetVosTVY2iL98FfRI48CzH6uLFi3x5oNLr0WiEZrOJSqWCer3OYUojyY48ej6fD9evX8fq6ioikQg8Hg97YgBwyejVq1fh8Xhw584dWCwWpFIpNBoNVCoVJJNJaJrGoVyv14ter8cJnxQKNpL8CH0JPwDuqVMqlXB8fIx6vX6iianR0DdvpbQHalZKLGO59etitVoRi8XQ7/dRLBZRq9Vm8ntmFlqiG5Hf78fm5iZWV1fx+PFjFItFWCwWWK1W2Gw2OJ1O1Ot1bjxlxIVBpeckh0gkgmKxiKOjIxwcHODWrVtIJBJot9sYDAbY3d3FysoKrly5Ao/Hg0AggO3tbe5/Ua1WYTKZUC6XkcvlUCqVOIS16AuL3j+5KhuNBtLpNCRJwkcffcSGjMfj4WdeliqSV2EymcDn8+HChQvY2NhAKBSCzWbjG1Or1UK1WkW9XudyYmDxb5T6hFNJkuD3+3Ht2jXEYjGEQiHWF6oyIUPG4/HA7XZjY2ODEzm73S7K5TKSySTn0kiSBJ/Ph9FoxLkzRu3qqs99IENmMBjwYUQl2CRLI+w7z4MaR1qtVjZkpjsbG/G53ySyLCMWi2E0GuHx48cz+z1v3JChD3c8HqPX66FareL+/fvI5/PY29tDsVjEysoKYrEYBoMBxxnJG2GkTYG8MdSPgVzUrVYLuVwO9+/f52oI6nA8mUzQarWQzWa5SiccDvOGSxn0ZOioqspVTUaTH/BNB2S6CZlMJmiaBr/fj+FwiOPj4zN+h28PlLxLJcZerxfANzKkgyiVSqFSqXD3WqNAhQOUJB+PxxEKhWC1WjEej9nzq+8Sraoq9yXSe/fa7TZKpRI3wQO+GetgFM/nNCQXj8cDu92O1dVVxGIxBAIBAEC/30e73WY50kXCiPKg3J9mswmbzYZms4lWq8VGrZH22FlCxqAsyzPVk5kYMiaTCaPRiMMCv/rVr6CqKg4PD1Gr1fDTn/4UgUCAu/7RQU7N8IyAPuFZURSEw2Gsrq7CYrFwsuqvfvUrFItFZLNZdDodXhylUgmFQgGVSgXlchmrq6twuVyIx+NYWVmB3+/HjRs3EAwG8dVXX+Hhw4doNBpcRmuURUZhSjpkAHDsdXNzE5PJBI8ePeI8pGVEf3N2uVyck3bt2jW4XC4AYEOw0+lgb28PX3/9NRKJBJrN5olxBYsK6QnwLPy4ubmJq1ev4vr163A6nRwGymazqFQq7HFwOp0Ih8Not9vcAI/K0KvVKvb29hCPx9Hr9fg2rs8Rod+9yLJ7HvF4HBcuXMDVq1fx3nvvQdM0AOAwdi6XQ7PZNOy6owvoaDRCPp9Ht9tFPp9HsVjkkRaCV8NkMkFRFCiKMtP2DjPtIzMajThbWZZl7lBLh5J+locRQ0qULOZ0OjlRVZIk1Go1VKtVlMtlVKvVbzUBJNlRTNput/OfkUgEJpOJy938fj/C4TAsFgu3hDba5kojCfT5DUbvcPwq6NeMyWRCIBDgMnWHwwFFUQCAZ+XQqItarWaYstHp8QOBQACrq6sIhUKw2+2wWCwcsi0UCigUChwuoMTNTCbD/XWozwWA516q6O8WXW7Pg/SBxqdQJ2SbzcaGcKvVOuGZMDo0Q0g/EFHw+izkrCV9smav18PXX3/NXwOe1eHrGwgZzaonQ0RVVW7+99577+HGjRt48uQJ7t69i52dHSQSCbRarW/NUKIZJ71eD4lEAt1uF7du3UKhUEAkEkEoFOIuwL1eD3/0R3+EVCqFX//614bqMUNGHbn+qakSAI5bA8s3zA84WVlB1YE/+clPcP36dVy+fBnRaJRd4MPhkA3nw8ND7O3toVqtLvw0cVpnJpMJoVAILpcLf/AHf4Cf/exn8Pl88Hg86Ha7ePLkCcrlMn77298imUwiEokgHA6jUqng8PCQw9/NZhP9fp89qXa7/UTb+WlZLarcXobT6eT2EIFAAOPxGIlEgsOS5M0zchM42nv0lyijXrhnhb4lxKzlNpPQEnDyISj2TAlTRgkfvQwq1aT21g6HA+PxGLVaDY1Gg8tfpzcD/Y1wMBhwgqbD4eB/QyXdLpcLkUgEvV6PcySMlvxK+R1U2gfAsJ2Nvwtms/nECBCv18vJqDQkkm7T7Xab53YtshGjh5rX6Zv/UZPJyWSCer2OSqWCQqGAXC7H4d5CocDtDKilAa0biuuT548uYuQ9NdrtXF8tSmMJVFWF2WzGeDxmT16z2USn08FwODSE7rwKRvqc54lePxbOkJlGnwz2vIxvo1q5lCNDY9+BZ5n/tVqNY/V0yLzoZkO37fF4jHw+DwB48uQJzGYzd7n1eDy4cuUKHA4Hzp8/D1mWkc/n0Ww2F7o8cDpOPR6PUSwWOcfB5XJx6MSI+vMy6LZos9kQj8fh9/sRjUYRCoWgqiq7wwGg3W5jZ2cHx8fHSKfTqNVqCz/FmQ5eVVVht9vx8ccf45133sF7770Hv9/P3rput4tkMol0Oo1kMonj42M0m03kcjnu4URGsslk4jXpcrl4FpqiKBgMBiiXyzzSodvtzqzd+ryh9UPjGWiEitfrZSPwiy++QCqVQjKZRKvVMtRF6WVMn1l0pi3L878KL5LFvEKxMzNk9J4Z2jCXpWxNn+9CXigAnDNE8fhXudWZTCZMJhM0Gg3IsoxSqYRMJsPt5xVFQSQSQb/fh9/v5z4hi96oizYLenar1cq3wclkwg26lpnJ5NlwTY/HA7/fz9PCqUqHvqff7yOXy+H4+BjVapUTyxfVkNHrNvWiWl9fxzvvvINYLMbDDcmDQmG1Wq2GWq3Goz70a4UOJvp3iqLA6/XC4XBw/xDyZlHPK6Mk1pMsKfeMkqBlWeZijKdPnyKRSKBarbJHeNly1PSHspFzpb4rLzrX5yGjuQxa0W86VqsVbrcbkUgEjUYDuVyOk8eM2BBPz7Sr7WVGDH3/YDBANptFvV7H3/3d3+HevXu4du0arl27Bk3TOMnzBz/4AWKxGLrdLtrt9olb+SLKkzx2jUYDkiQhn88jl8tBkiRuEGiz2bifxSIbbq+Kvk+TpmkIh8P46U9/ipWVFayvr3Neh8lkQrvdRrlcRiqVwr1797jU3yg4HA68//77CIVCuHz5MtbW1qAoClqtFnv0aELxcDhEq9XinlWyLJ9IWNU3QKOcm83NTR4USEnB6XQazWbzRNm6EXTOYrFga2sLwWAQFy5cQCAQ4EKNTCaDZDKJVCrFU+eN8MyvCvUL6vV6PDfQ7/cjEonAYrGgWq0axjv3XTkt3DoPj/lcJ8ZZLBbOiA+Hw5hMJnj69Ck35jJap8g38cFSsmY2m4UkSUin07BarSiXyxgOh9jY2EA0GoXP58MHH3yA9fV17OzscG+ZRe3cSpsCeWRGoxGKxSJyuRzcbjfC4TB8Ph9kWea28ssAeQxoGGssFsMf/uEf4vz589A0jY0YSZL48E0kErh//z4nly8y+rVjt9tx/fp1bGxsYHt7G2tra2i1Wmg2m7zXUNXJaDTiHiidTofLqUlW5F2hy1YwGMS5c+d4n+p0Osjn8xyaolDUonsl9D11zp8/j4sXL2JzcxOBQADZbBaFQoGnPR8fH7M3ZhH3lFeF9h2CDBkaKkqGjP4MM2qKxJti4XNk9JDyU7jlebkh00pkFGjTczgcCIVCKJVK3woBvMrmQN6HXC6Hhw8folarcQimVCqh0WhgMpmwe90oVUyTyQTtdhu1Wo1LQhVFgaZpfEgtsvfpVSHj2GazsSfObrdz+TF9Dw2IzGQyJ7yeiywjenaaWxaLxbC2toZYLAaLxYJut4tUKoWjoyMoisLTqsnwpT47+guG/k/yxHg8HqysrCASiUBRFM4TyWazyOVyfDlYRBnq0Sc222w2eDweLt0nbxYNp6WmpUbcm0+DukHXajUUi0WEw2EOwdH8pUUN0c6LeayVuRoyALi0kRoLUamx0a186vAbCoVw8eJFNBoNqKp6ohLitOenv6PKnZ2dHRweHsLn8+Hu3buQZRmKovAhRmWTzWZzXo84UyaTCarVKnK5HAKBADRNg8vlgs/ng8lkQrfb5WFlRocM1c3NTZ5MTE3LAHA4pVgs4sGDB5zgS/lFi7rx6hvZ0SDI999/H7FYjPsz3bp1C//3//5feDwerK2tQVVVLiO+c+cOisUiD63V/9zxeAyr1Yrt7W1sbm7ixo0buHTpEur1OtLpNI6OjvDw4UOuclp0Q4ZkaTKZYLPZoGkaVlZWsLW1hUAgwH2pKDeG8vroAmp0pvt5HR8fw+FwIB6Pc16Wz+dj798i68KsMZwhI8syVFXlEfDkMaDQgFHcc/oPjQwPcm/Lssyl2Iqi8N+9Tsk0uTonk2eDN7PZLFRV5Zgt9Zghd7gRoMOGYtH6eV36fjJGz5OhpEzyOLhcrhMbKa2pZrPJXVhLpdLC36b1uXN2u529UeSdq1QqaLfbyOfzyGazaLfbkCQJTqeTqwapTF3fkBMAH+b0c6PRKJxOJ8xmMydKFwoFHlJqpCRf4Jlc6HJJlYCdTgeNRoNLrskTvGzQnjI97dpiscDhcHByPUUXjL7/PI/T9hW6TDcajZmOQ5mLITMej2GxWBAMBhEOh+H1evkWTe7adruNfr9vmCZLkiRhNBqhWq1yAmK/34fL5cL58+dRKBQQj8dRLpeRSCReOVmMvocMIxpl4PP5sLa2hkAggJWVFbhcLvzt3/4t9vb2+N8tOmTs0owcTdPgdDrR7XY5vGZU9CWytJauXLmCaDTKB7XJZMJ4PEY6ncbjx49x9+5d/N3f/R1X6CxqIj0dJtQEMRqN4uOPP0Y0GkUwGIQsy9jZ2cH+/j4+//xzfPXVVzyQ1ufz4Q/+4A/YY+Xz+bgNAgDum0J5IZ988gnee+89eL1edLtdJBIJ/OY3v8Hx8THy+TwnEi+qV4ugiwEAKIoCVVXhdDrhdrvRbrdxeHiI/f19PHz4EKVSiftdLZrufF9ITmTY9ft9TCbPZnptbW3xcORWq8WXUsAY++1p6FunPC/Rl9ZIt9vFzs4ODg4OUK1WZ/Z+5la1BIBvPdS0bTgc8tBIfaMpIyiB3uPUbrfZ80R5Mk6nE16vF8PhELIsv3aDKVpgpFDUSdjpdHIprt1un+ETzg+SC+kM8M2BTsmZgPH7yVC1kqIocDgc8Hq9cLlc7OrX940plUoolUool8snwiiLDFUiybLM3ijy7tZqNeRyOZTLZdTr9RMjO+ggpq7QhH4Wmt/vRygUYk+PyWTiRpTZbBbFYpE9x0YKrZAhR/ke5LGi+VPkkTGKF+q7Mj2qgDo/U5Ug5RQtC/oWIy9qqUKhOZoDOL3+3iQzNWTIMKGbNG3AkiSh0+mgWq0inU4jn88vvOtbD1mjg8GAyzV3d3fh8XgQiUQQiURw+fJl/Mt/+S9xfHwMk8mEQqGAUqnEh45eKabLtoFvvBM+nw8bGxtYXV3Fz372M4RCIa7MMFqorlQqQVVVdDod9si43W7ui2KU552GNoTJZIJgMIiNjQ1cvXoVV65cgcvlgs1mAwB2fafTaTx48ADJZJIPX30zykVDHwLSG3J2ux2j0YjX2e7uLorF4okeMtTMzWKxcOl1v9/nEOzGxgZCoRD+6I/+COFwGJubm9A0DTs7O9jZ2cGjR49w+/Zt9hgbJZePjEKHw4F3330XsVgM586dQygU4mff2dlBMpnkRPplzgWZXjtmsxmKonBo22KxsLdmGWRE+VWqqnIXaEVROMxPk9JrtRoPZF340JLeLW6z2SBJEpezNRoNjsHqrbxFhw5W6qJKA+t8Ph9UVUUoFMKVK1fgdrtx+/ZtDIdDNBoNju3rZfG8xUFGoqIoiEajWF1dxYULFxAKhfD48WO+RRkBkgEtjMFgwPkxqqqyThkZMtIcDgei0Sii0SjC4TDsdjvMZjMnefd6PfYilMvlE0NEF1lG9Az6fA6r1cr5C/V6HcVikfuckPHX6XRwdHR0wvVNBw8lb8bjcVy5coXD3larFaVSCQ8fPsTBwQEymQz3nFn0kBLw7dygWCyGlZUV+Hw+nt9GobRKpYLBYABZlhdaf74v014Hfam+vox/2ZBlmV/U0gAAh+MoGjHr3LI3bshMu5wsFgvcbjdnedNEVSqdpZgs8Mx9px9kZwTFGI/H6Pf7ePToEd+O3W43JpMJ5zf88R//MQqFAm7duoV0Oo1KpYJSqcQ3S308m5SF8gU2NjbwwQcfIBQKYTgcolQq4fbt29jd3cXR0REAYxiGANBqtVCpVDjfyGQywev1ot1us3vXKM/6POgWpCgKb6D6arZHjx7h+PgYd+7cwe7uLlfXLPrhS89IFSTU6M9isWBtbQ0OhwPXrl2Doih48OABFEXhDtrANwNGKQk+EAggEAggHA7j0qVL8Hg8iMViUFWVK3Ru3bqFO3fuoFwuL3TJ+jT6Enav14uVlRVcvXoVq6ur0DQNg8EAjUaDWzkYZR9+U5CBbLPZEAwG0Wq1eJQDHdhG98roq/w8Hg88Hg/cbjePjaEeVrlcjqsEyZABZrOOZu6RsVqtCAQCcDqdHNemeKzdbofH40Gj0QAAjkHqRxosOnpDJpfLsTvb6/VidXUVgUAADocDtVoNNpsNu7u72N3d5fg+9f6g+CJZvpqmwW63cwmqw+Hg3iF37tzBzZs3USqVzvjpvz/6jZQSLal6hAwZGt+g76NiBN3RM+3VpOclY2YwGODRo0d48OAB7t69i93dXe5dZIScDkpw7/f7aLVaKJVKsNlsMJlMsNvtuHbtGmKxGGw2G3q9HldqAc+mOdtsNu5Yu7W1ha2tLfj9fmxsbLBnbzAYIJVKYW9vD3fu3MHdu3c5/8wo0CFEnty1tTU2ZCwWC3q9HhqNBorFomEM4TeJvo9TOBxGu92G1+tFuVxGpVI567c3F0iHTCYTXC4X52TSOiNDJp/Pc7XfrMNuMzFkJpMJJ+V5vV5cv34dPp+PNxK/34/xeAxZluH3+9Hv9/lmRXF+Sq4CFtugofdOVSMHBwe4desWotEouycpefHChQvQNA1erxeRSASDwYA7HlPJKCWXuVwuuN1u7j4KPMshqdVqKJfLnGFvpBvVYDBAp9Phcr7xeAy/349Wq8VeCn0mvVGe+zQmk2ezlDqdDk94Jg+n0fKF9MmDyWQS4/EY5XIZAKCqKoLBIC5evMgNzNbX1wF8Y8isrKycyFNTFIVHYFB/mHv37uHg4IAHlRpVj8hbTjlHVquVD51UKoVMJsNVJkbaQ74P+rDSeDxmD4x+vM6yQRep54XW9JGE6aqmN81MQkuUGEYTZP/iL/4CGxsbCAQCsNvtrAjU1Mvj8aDdbqNSqeDg4ADlchntdps3ZGBxjRm6zRSLRRSLRYzHYxweHmJ7exuDwQB+vx9XrlxBIBCA1+vFYDDgxF9KmBoOh2i325hMJnC73VAUhd3jsixD0zSUSiXcvHkTyWQSiUSC+8cYYRPS58j0ej0Ui0UcHx8DAM6dOwez2QyXy8VN3163AmwR0OdN6XMcJpMJms0mqtUqEokEdnd3US6XDXUA66vWxuMxCoUCbt68iVwuh8uXL2M4HCIUCiEejyMWi+GHP/whe23IY0PGP3ViNZlMXHBQKBTwm9/8BtlsFr/97W+RSCQwGAwMOz+HLprkFadkzcPDQ75o3b9/nw8h4ZH5poCDZDEajdBqtdBqtbjy1ig5ia+Cfg8iz7DemNGnRczDwJtZaElfOdDv909MjKWHttls8Pv9MJvN7JEplUocOjDSZkwfaKvVQrFYhNvt5jlToVCIB9lZLBY4nU4A4PJ0SloEng3Ks9lscLvd3J6fvDDUuIv+jdE2YUoW63a7aDabsNlscDgcJxLNlqUpFX22k8mzOUCtVgvtdptHERjts9fT7/dRrVZht9uRSqW4JJr2FlVVORQF4ETYkRqbUdPAQqGAYrGIdDqNXC6Her2Obre7NLdrfT4i7cvk+TKyR+p1obAm6RQNIW21Wjy+YRlL1PWVycRZ7D1v3JChBUH5Gvl8Hl9++SUSiQQuX76McDgMv9/PHgVqUHX16lVUq1We3EyGDP3MRYcqS6gvQ7FYxP7+PoLBINLpNMLhMC5evIhgMAhN07C6unrCmtXfjmiTHo/HnNyZyWTwf/7P/+G4JGAMbwzwjTeCNtZyuYynT58iGo0iFovB4/FwDwwy+Iy2AesPVroF0cDMZDKJbDaLTCaDUqmEXq93ImneKJDut1ot7O/v89wjn8+Hjz76CKurq9jY2MD6+jpMJtOJvLHhcMht9hOJBBKJBGq1GlKpFJrNJvb39/lQMkKV1+siSRKCwSBGoxF8Ph8sFgtPDTfaWnodaO+hBGiKEtTrdS6ooKakRr9A6NEn4OtzyPR5efOs5pqpR2Y4HPIoeADcaMpkMvEtmqovVFVlr4RRFUGfsEieKjqIBoMBvF4v3xxJDtOyoFr8brfLSY00mbZQKKBcLnNujNEgw67b7aJWq8HtdgPAiQVjxOeeRj+ugUqPq9Uq9zqhm6ERZaFfQ9TFuNlsIhaLwWQyQdM0eDwellG/3+eKEvL2plIpHB4eolarIZ1OcwNBSiA3otz06C8GZKyMRiPel41oBH9XSAZULddsNnm9lUolVCqVEwM1l01mNIJH/yKdmmfu0EwMGX1JaK1Ww+9+9zsoioJ79+5xV1uXy8V9Iagqp9fr4euvv0Ymk0Gn0zFkbJaS7ABwvsdnn30GVVVx8+ZNbvRFuUQvgpSkUqkgmUyi1WqhUChwmZvRZEd5VZPJBOl0Gr///e9RrVbh8Xi466qRNxE6fOhyQPlktVoNX3zxBdLpNLLZ7FLcoMnYGI/HyGazbKA4HA6Ew2EEg0E2evWT0al6gipMqJcV3SrpEmFk2QHPnq/f76NUKkHTNOzt7aHRaCCXy3EzTeAbr+ayhNmm0VdsUdjxl7/8JR48eIBGo4FMJsPzqKZDK0ZGnydUKBTQ7Xbx9OlTuN1u+Hw+VKtVPH36FPfu3UM2m0Wr1TpRuLMQ5df6ZB+6PSeTSUiShHQ6zY3MqH0+LRTaTPRTevVZ4kZB/yxkvR4cHMBkMiGVSnGjL1mWT/05lH9EDdD0U7T1/VSMtAmRTtVqNSSTSaiqimKxiEql8q0SWSPpDEFrZTAYcDJrqVTC06dPOUSij9MbUQYEGTLUuqHZbMJsNuPw8BAOhwPAN/pC3hvKcaDkTH21hdF7EBH6/bbVaqHRaLDHnEJvRuqy/n0hnWg0Gpy7eXR0xAUYdLFYtnAkzXVrNBo8BoSKWSgh//j4+MRoj4WqWtIzfZjqQyr6Vvy04UwmE86NWYaFpFd6StqkeUwv86iQjCiB+nkLyWgyJJl0Oh1uRd/v97n5UqvVMqQ3Cvgmx6pQKODevXtIJpN4+vQpOp0O9vb2OHyyDEYMQQaI3vtCVWuEPreK8soAnGi3v0wHEAAen0KehF/96ldwOBw88+7g4MBwnda/L3rvHu2503vvsqG/WN2+fRvpdJpHFVSrVRwdHaHdbp9Inp+VnKTTDju32/29T0J9yejz/vvEm5mqR/8uD12r1eamUXa7/Y1YCtNyeR0DRO/+fBV5tdvtuclH07SZWFLTugTgjeU2tFqtucnH4XC8tnz0OjK9Ocwjv6PZbM5NPk6n85Xlo0+Kn14/0/vJLA2XRqPxVuuPnmmZ6eUzq8vAvPTnTe89z9MvfbXXm+Bt33texIvOLf2+9CY4TXfmMmsJePkhu4wWLTFdMvyqcWm9zJZNfnoZLduzA99+5mWUgZ4Xld0v8xp5HYScTud5+rVsnrwXoZfN9J48L/nM3JDRh5de9eGWUTmWKSTwfVlmWek3T72xu4yymEYcLK8HycoIIyzmgdCvF3PW4fy3xiMjEAheD7GmBAKB4CU5MgKBQCAQCARvM8Yr7xAIBAKBQLA0CENGIBAIBALBwiIMGYFAIBAIBAuLMGQEAoFAIBAsLMKQEQgEAoFAsLAIQ0YgEAgEAsHCIgwZgUAgEAgEC4swZAQCgUAgECwswpARCAQCgUCwsAhDRiAQCAQCwcIiDBmBQCAQCAQLy1wNGUmS/q0kSTclSepJkvSf5vm7FwEhn9MR8jkdIZ/TkSTJJ0nSf5UkqSVJ0pEkSb846/f0tiB05+UI/XkxZ60/c5t+/U8cA/gPAP4FAHXOv3sREPI5HSGf0xHyOZ3/CKAPIAzgOoD/LknS3clk8uBM39XbgdCdlyP058Wcqf7M1ZCZTCZ/DQCSJH0IYGWev3sREPI5HSGf0xHyeTGSJGkA/gLA1clk0gTwuSRJfwPgXwP4d2f65t4ChO6cjtCf0zlr/RE5MgKBYBm4CGA4mUye6L52F8CVM3o/gsVC6M9bjDBkBALBMuAAUJ/6Wg2A8wzei2DxEPrzFiMMGYFAsAw0AbimvuYC0DiD9yJYPIT+vMUIQ0YgECwDTwBYJEm6oPvaNQAiUVPwKgj9eYuZd/m1RZIkBYAZgFmSJEWSpHlXTr21CPmcjpDP6Qj5vJjJZNIC8NcA/r0kSZokST8G8HMA//ls39nbgdCd0xH6czpnrj+TyWRuLwB/CWAy9frLeb6Ht/kl5CPkI+QzU/n4APw3AC0ACQC/OOv39La8hO68koyE/rxYNmeqP9I/vQmBQCAQCASChUPkyAgEAoFAIFhYhCEjEAgEAoFgYRGGjEAgEAgEgoVFGDICgUAgEAgWFmHICAQCgUAgWFhOrfN2OBwLV9LUbDalef0uTdMWTj6tVmtu8hH6czp2u33h5NNut+cmH4/Hs3DyqVarYv85hXntP2JtnY6iKAsnn263+0L5CI+MQLBkiJYLAoHASIjOjW85U02HYDKZIElzM9wFBoH0ZzweAwAkSWI9WhZ9Ihno19M0yygXwatzmg6ZTM/8AkJvnqHfa/R/zgJhyLzFTC8WcZMWfB/0mzCx7JsuyUIvh8lksvRyEbwY2penD2qhN9/wPEPP0IaMXikmkwl7HPQ3o2WEZGGxWCBJEv/Z6/UwHA7P+u29Feg3E73+6HVo2aHNlWRis9lgsVgwHA4xGo34ZeRNWG+sSJIEVVVhs9ng8/mwvr4Om80Gp9OJ8XiM+/fvI5fLod1uo9PpLK0e0b48Go0AfHMImc3mpZQH7S9msxlWqxUWiwU2mw3AN7Kivfk0j9+yYLFYYLVa+f8nkwnvOTP5fTP5qa8JPSQpiv4wWkb0YSRZlmE2m2G32yFJEiqVijBk/gm9AUz6Y7E8U+ll1h9Cv5mazWaYzWY4nU4oioJOp4Nut4vBYDCzzeVtgYxdi8UCk8kETdPg8/lw/vx5/PjHP4bb7UYsFmOjrtfrYTweo91uA1gur5UkSbymRqMRBoMBf11vEC8b4/EYo9EIVqsVsixDVVW43W5IkoThcIjxeIxqtcpyW2ZDhs5xOrPIsGs2m8YyZKZd2zabDX6/H2azma1aei0j9MGrqopYLAZN07CysgJJknDz5k2kUqmlvSkC3+iPxWKBpmkwm81QVRUmkwntdhuDwQCDwYD1Z1nlBDyTlaIoWF9fh8PhwOrqKlwuFw4PD3F4eIhWq4VOp/PcEMsiw8Pk/smbaTabEY1G4XK5EIvF+LW5uQm73Q6fz4d+v49wOIxoNIput4tCocCXqmVBf5m02WzQNA3As4N8PB6j2Wyi3++f8bucH+PxGJIkQdM0WCwWRCIRRKNReDwerKysAACGwyH6/T4ePHiAfD6ParWKarXKht8yQPsHXZhCoRAuX74MSZLQaDTQ7Xbx9OlTlMvlmZxdZ2bIjMdjmEwmmM1maJqGK1euQFVVZLNZ1Go11Ot11Gq1s3h7Zw55GDweDz744AOEQiH86Ec/gslkQqFQQDKZBGCcQ+d1If3RNA3hcBgOhwPnzp2DLMvY29tDqVQ6sZkAyy0rp9OJTz75BPF4HB9++CFisRj+x//4H/j1r3+NfD6PYrGI0WjE3iwjoN9jrFYr7HY7bty4gfPnz+PSpUu4dOkSHA4HAoEAH9ydTgcXL17EcDhEvV7H/v4+b8zLoD9k/NlsNiiKgnA4jCtXrkCSJDSbTXS7Xezs7CCfzy+NPEajEcxmMwKBADweD37wgx/gww8/xOrqKj744AOYTCZ0u100Gg38l//yX3Dnzh08evSIjWBZls/6MeaCXndUVcXFixfxi1/8AgCwv7+PcrmMarWKYrE4E6/eXHcuvdVmsVhgt9vh8Xjg8Xhw7tw5dkUpisKbiT7euCyLB3j2rFarFU6nEy6XC06nk2+XRs5neBVIJ8xmMxwOB7xeL9bX16EoCur1OkajEbrd7lLpjR59aJJu1eFwGJFIBLIs8w2y2+3y7dooMtI/u9VqhdVqhd/vh9PpRCwWQzweRygUgsfjgaIokGWZb85WqxWapsHlckFV1RMGzDKsOVpXtDf7/X5sbGzAZDKhXq+j3W4jmUyiVCoZPg+E8u00TYMsy4jFYgiFQlhZWUEkEkEgEIDD4TiRx+jz+eD3++FwOPhry4Ysy3A6nfB4PAgEAhiPxzg+PuYUiVkxV0OGYq+0WZw/fx6ffPIJgsEgPvzwQyiKwqGTL774Avl8nv/NMqDfhE0mE5xOJ1ZXVxEIBKCqKobD4VIuDj100x4MBrDb7dja2sLq6ir+1b/6V3C73fD7/Xjy5Am++uorJBIJvm0vEyQjh8OBeDyOc+fO4Z/9s3+GSCSCJ0+eYGdnB3fu3MHjx485B8IoMqIQiMPhYIPl448/RjAYxA9/+EOsra3BbrfDbrcDAMfsZVmGyWRCOBxGv9/Ho0ePoGkaRqPRiXVn5PVHe63b7cb6+jo++ugj/Jt/829gsVhQLBZRLpeRz+dRKpXQ7XbR6/UAGE8mtH4URcHFixfh9/vxp3/6p9je3kYkEkEoFILVakW/3+dLuaIouHDhAqxWK5rNJpLJJPr9PodtjbK+XgQZtoFAAOfPn8fFixexubmJdruNhw8fzjTRFzij0JKiKPB4PAiFQlhfX0cwGMTKygpkWUYgEECz2YSiKGfx1s4cuhFR3ofT6YSmabwQjH4TOg29h8VsNkOWZXg8Hvj9fkQiEbjdbvh8PjgcDlit1qWVFT231WqF2+2Gx+OB1+uF2+3m3I9qtYpWq3XC07DIB9L0JUBVVb4h0206GAzC5/PxLZoSOCkhEQBUVWUPqKZp6PV6hq/qmkaWZfZ0RqNR9i6Qt5w8VUaUyXShBe0tKysrWFtbg9vthsvlYmOHQk+SJHGo0uPxwOl0cv7ZMmGz2eB2u+F0OmG3209Ulc6SuRgypBxU+nnjxg38+Mc/xsrKCt5//31eHN1uF+VyGblcDs1mc6m8McA3B5DP50MoFML29jYuX74MVVUxGAzQbDaXPgF6MpnA5XJB0zRcunQJH330EcLhMMxmMzqdDlqtFlqtFgaDwdIlROsNPZPJBL/fjxs3bnCCryRJSKVSePjwIbLZLMto0StR9GX4fr8ffr8fFy5cwE9/+lN4vV5sbm7C4XDA4/FAkiSMRiMOqU0fxvF4HD6fD4VCAd1uF+l0Gnfv3sVgMODfY1S9ossS3aojkQgsFgtGoxEKhQJyuRyHmMhLZSQ56D0xbrcb8Xgcf/Znf4bV1VVsb28jEAhwSTHld47HY3S7XQyHQ6yvryMWi3FCdCKRwJdffsm6YyRZTUO64PF4sL6+jlAoBEVR0Ov1TlwUZsVcPTJUex+LxXD9+nX2xOirTdrtNmc5L9ONWn9Q2+12BINBhEIhPqSz2Sy63a7hS2VPg+SjKAp8Ph+CwSDW19f5gBoMBuj1enyLNvLG8TKo0mJlZQXRaBQ2mw0mkwnVahXZbJZziWhDNgJU6Ud68f7778PtdiMcDkOWZfas6A0ZAOyhMZlMfJtcXV3F1tYWxuMxHj58yCEmI0PrhfYfp9MJk8nEl6harcYl+0Y8mPU5QpqmIRAI4PLlyzh37hw8Hg9UVX1uzia1MPB4PLBarVhbW8PW1ha63S7MZjOHb42MXne8Xi+cTievqXkwc0OGrFyz2YxgMAiPx4O1tTVEo1FomobhcIher8elWXfu3MHOzg6y2ezSdiDVNA2hUAh+vx9utxu9Xg/Hx8fIZDLc22KZmNYDv9+P7e1tbG1tIR6PQ5Zl1Go1NBoN7O7u4sGDBygWi0uXH6MvS6dEccoToXVWrVZRKpXQ6/UWvrRYX2Lt8/lgt9vxwQcf4IMPPsDa2hrC4TB7e0k24/EYu7u7ePToEf97TdOwvb3N4RRFURCNRnH16lVomoZOp4NyuYz9/X32Rkx3dTUC5J1zuVyIRCLweDwAgF6vh3Q6jVQqhUaj8a0meUbD6XRie3sbGxsbHKqmir5Op4NOp4N6vY5MJgOTyQSfzwebzQabzQZZlrlHUbFYhNVqZcPPqMYfFaaYzWb4/X6sr68jEAjw3wOz15W5eGTIkAkEAojH41hZWUEsFgPwrAa/0Whgb28PmUwGd+/exYMHD07E1Iz24Z8G3aRDoRB8Ph9cLheq1SoymQyOjo7QarXO+i2eKZIkIRgM4tKlS9jc3EQ8HsdwOMSTJ0+Qy+XYkBkOh7z5LIv+6G+UlF8VDAbhdrsxGAzQ6XTYkKHb4qIzHo9hsVjg8/kQCATw/vvv42c/+xlcLhdCoRBMJhN7YuhStbu7i//5P/8nh679fj9kWeZSfofDgUgkwk3PJpMJMpkMSqUSRqPRidwaI0CHEXnnnE7ntwyZ4+NjJBIJNBoNDIdDQ5ekO51ObG1tYW1tDV6v90SOIqU/pFIp3Lx5E6qq4tq1a1x9CwBerxfnz59HMpmELMtL0Y2dGgX6/X6WG/BNH55ZM7fQEh3Q5KIzm83o9/toNpsolUrY39/H8fExu7yXEfrAKeGQcmPa7TZyuRyOj4/R7XbP+F2eHZTLQZ1ZHQ4HAHCpfrVaPeH6Xib0z+t0OrncmuL6mUwGlUoFlUoFnU5n4Svg9N4nWZYRjUaxurqKcDjM3YsnkwnvMb1eD5lMBvV6HQ8fPkQymWTjptFo4P79+8jn81xuS8mbfr+fm1G6XC60223u/LvouUXT6MvQyZNFBnCxWEShUOBKJSNBl2ZN02C327GysoLNzU1uVwCADdhisYinT5/i6OgIjx49gsvlwvb2Nv8sfbn+MuxBZASrqgpN0+B0OuFwOLhYZzQaoVwuo1Qqod/vz0wmczNkyAUXj8c5lthqtZDP53F4eIjPP/8cyWQShUKBY/eL7PZ+HaaTNF0uF1ZWVnjjLJfLePToEfb391Gr1Qy1eb4MveLTjTEQCGBzcxOhUAiTybMZJ9lsFul0GvV6Hb1ejxuZLRuSJCESieDdd9/FO++8g/Pnz6PRaOCzzz5DIpFAIpFArVZbaPnom93JsswNNa9evYpLly6xXlA/oVQqhWq1ir//+7/H/v4+njx5gsePH/PPsdvtKJfLXHFC/a2CwSBUVeWeKv/4j/+Ifr/PCeVGW4e0vhRFgdPphCzL6Pf73Bxwf38fzWbTcM9NHjufz4dz587h/fffxyeffMJFBQC40/z+/j4+//xz7O/v4x/+4R+wsrKCTz/99MR8QNK9WVfqvA3Q/ux2uxEIBBAKhRAIBGCz2dhZcXR0hL29PTSbzZm9j5kaMtMN8Kh/DA3bGgwGaDQaaDQaaDabaLVahnfBnYbdbueSYq/XC5vNxpU4NMRuGb1V+jgs3RY1TYPVasVwOES320WlUkG5XDbkjfFlTMehHQ4HwuEwPB4PzGYzRqMRKpUK58Ys+m2RDH5yZbvdbgSDQQSDQW5SRv1kut0ustksisUiMpkMdw6ng2YymWAwGKBWq8FisaBcLqNYLEKWZbjdbm4qSN1uFUUx7AWL1paiKFxhSsYeNVE04uFMxoeiKPB6vWzAUOQAAOr1OlqtFnvGi8Ui58vQGbZsZ5c+74fyy2hfBp6F4drtNr9mGY6dmSFDD0k9HZxOJ9bX17G9vQ2fz4fxeIx6vY7d3V0kEgmUy2VOJDOay/Y0yHq3WCw4d+4cotEobty4gevXr6PdbiORSODo6AilUgm1Ws2QZY8vgxYL9XAIh8MIh8OwWq1oNBrIZrP43e9+h8PDQ1QqlaWSjR7qP7S5uYlPP/0UgUAAg8EA1WoVOzs72NvbQ71eX9j1pc8BkmUZXq8XP/nJTxCNRvGjH/0IW1tbUBSFD95ut4t8Po9f//rXODo6wu7u7rfGMZBBVygU0Gq1cPPmTdTrdXz44YdwuVw8KFBVVXi9XjQaDZ51ZhQoh9Hv98Pj8SAcDvPsO70hQ4NZjYL+AkBzlK5cuYLz589zCgQ1vrtz5w57Yb744gsOYVPDt3a7jUgkgmAwaCjdeBXMZjO2trZw+fJlrK+vQ1VVNvqSySRyuRzK5TL6/f7Mzq6Ze2RoKKTdbudEOlmWMR6P0ev1UKvVUKvVToxAXzZFoGd2Op3w+/18K6CF0mq10Ov1MBgM5pY89bZBzbgoBkulkGT1V6tVVCqVmS6Wtx2qVnK5XBwWIflQVZcRDGE6eOx2O0KhEKLRKOdMmUwmTCbPZpXRbTmXy3GOULPZhMVi+daoD9Ib6l5br9dPdD2m7q2KopxoEGeU/YrWl8vlgt1uZ685ebb07SGMBlU3UkiRGmpSn5jBYIByuYzj42M+lEkWo9EIzWaT9cWI8jkNWhtOp5MrB6n3UL1e575DlFc2K2bukZFlGefOnUMwGMTq6iqi0SjMZjNqtRqOj485yY7CSnRbNMLm8Crom3mtra3h6tWriMfj0DSNm1Dl83l0Op2ZJku9bUyXXFutVmxvb+P8+fPY2tqCz+dDsVjEwcEBnj59iuPjY+TzeW7AtCzoPXqrq6sIBoPY3t7GuXPnUCwWcfv2bRweHrI7vN/vL6xHhg5VTdO4B9W7776LlZUVeL1eDqONRiNkMhncunULqVQKBwcH3ADQarV+q5Mx7VWDwQAHBweoVquIRqO4ePEid2vt9XpYX1+HyWTC7u7uifyiRTZm9OF/Wlurq6uw2+1cZtxoNAx30dTnWbndbmiaho2NDWxvb3PvLqrWKpfLuHnzJm7fvo1UKnXCoBuPx2g0GnwZJx01MiQ7fefwjY0NbG1twe/384Xg9u3bODo6QqFQQK1W4wjNLPRnpsHeyeTZYD+v18s9ZKjJEi2SbDbL2fBG70/wIkjxqXmXy+XiFvuUP9Tv95euVTpBIwmCwSDW1tbg9/tht9sxmUxQLBY57NZsNjkOu0wyok3V4/Gwe9vr9UKSJKTTaRwfH6NWq808Tj0PKLREnXojkQii0ShUVeW/H41GqNVqePr0KQ4PD0+ErV/UW4j+XalUQjqdRrFY5A7RsizzbZ1y1xbVGHwRZrP5W8UYkiSh0+lwI059p3UjPLu+wabT6eRzyuVywWQyYTgcolQqIZfLIZVKIZlMol6vn/gZFFnodDrsMV8G6ByivFdqf0AzzDqdDu89VDU4y2jCTDwy+tu0xWKB1+vlCbR2u51d3dTTolqtstGzTOhL1+x2OzRNg6ZpmEwmaDabKBQK2Nvbw/HxMSf66rPjjQ7JR1EU7u2xtrYGVVU5N+bBgwdIp9MnEsWXQTZ66KazsrKCS5cuwev1ckl6IpFAOp3mjXZR+3/oe504HA6srq4iFovB6XRCVVXuIkpx+Z2dHXz55ZcolUqv1ERSHyqiW7X+4kC/20iJvvRsFJL0+XyIRCJwOByQJAmNRoM9nrVaDZ1Ox3ChbZPJhEAggEgkglgshkgkwnkxlUoFX3/9NRsxtVoNg8GAw5dU3p9IJNDpdJBMJhEKhdButw3djJOe3Wq18jTweDzOnqxqtYpisYh0Oo18Pj+XJOg3bshMhwQsFgtPJSZDhuJnlNfQbDZn6nZ6G9EnQ1MVDhkz5IkpFAo4ODjg0BKFEJZBRnSgUG6CpmkIh8NYW1uDoiic+/DgwQMUCgWeQ0UH9TLICDg55C4ej+PSpUvw+XzcaDKRSLAhTKHbRdtg9V4As9kMTdNOGDL6vJV8Po979+7h3r17+Oqrr75T3yV9gut0Y04j7lE0gNXn83FTQABoNpvY39/nkn2a5Gyk9UUzyahJazgc5pEM1WoVd+/e5dAsVbbpq7kGgwGSySQngcdiMa7SNYqMngeFlmKxGNbW1ngwa7VaPWHIFAqFuYxomNuOpt+MaJOg2TiU+2HkD/556JOh9cbMaDRihahUKmg0GkspHwDctZU61Gqahn6/j3w+j3w+j0KhgHK5fOKmuAxyoo1U7951u91wu918m67X65zkS/kARoAMGr13kl7U96TVaqHf7z+30kafuKrPdSCvMJX5y7J8wktsJL3Sl86SR1hVVQ7RTYf+9aFto0CJql6vlz1RALjUvNVqoVQq8fNPezPJg0ff3+12T3jOjbLeToM8odSXqt/vcwEPhfvn4aSYedWS/kWbDTWq6nQ6aDab6HQ6kGV5KT54Qn8QOZ1OeDwe+Hw++P1+1Go1FItFJJNJJJNJzv1Y1LDA66DfKClZfH19HdFoFLFYDH6/n93djx49wpMnT9Butw3ZafU0KKfDZrNxfDoWiyEej2MymXClDjWDoxLbRZcPHRAUDpkO+zSbTeTzeVQqFZ5KrO/98iKjhg5parBHBzt1dtX/fqNAHl6qOKGBmQA4dLuzs4N8Ps8ez3kOApwVdBaZzWbYbDasra3hnXfe4flAo9EIrVYL1WoVBwcH2N/fR7/f55L96Z9FRkyj0UC1WoXJZIKmaYbSFT3TZeuyLHPrh1arhUwmw7MBm83mXPaemRkypCg090VVVW5URZ4Y/TCtZUWSJMiyzJsmNXlrtVpcqbQM01OnocOKSmzD4fCJJnhkAA8GA0OUFH9XKGeEwraKonClCYUDqEmgEWREN2BqEDkcDjkRldYSlefTqIHTnpn+nhrA+f1+7vBL3Unpxk3NKcnLs+j7Fh1ElMRMciPPVq/XQ7vdRrfbXfhnfRHU8JB6xkzr17RXT69L5Gmg1iKapnF+EXkEjaAn09DFgUKSeicEebKoz868mgTOLNnXYrHAZrPB4/FgfX0dGxsbsNls3Dsml8uhVqsBwMJb+N8VslQ9Hg/nEGmaxmV/1KSLBvwtg5xo4dM4+NXVVXz66adYW1vDysoKVFXlxDoqJ16m3CFC395gY2ODh7EGg0EcHR3h5s2buH//PrLZLBsyi+qxovdMBku1WsWTJ0/Q6/VQLpd5LpDJZEI4HMa1a9cAAA8fPkSz2TxRrfW857darfB6vXA6nfjJT36Cra0tXLlyBeFwmMtrqdSfkj4XOfFeX3LtdDpx48YNrKys4MKFC4jFYiiVSlyEQRWBRsxh1A/J9Pl8PJ+r1WohnU4jm82e6GZMoSTypo9GIyiKggsXLiAUCuH69eu4du0ae4wzmQzvT0aB9IBGWVAUwWKxcIdsOrvIEJyHJ3gmyb76bHgqXbTb7RxW6vV6aDQanIi3iJvBm4AsW0rypaRFaq1Oi4AMnmWBDGGn03mi/TxtNOTGpeRDo914Xob+ecmNTYPabDYbV1w0Gg326On7pywyz7sx60uDVVWFz+eDz+eD1+tlA/dFXjvqUeTz+eDxeBCNRrkEmWYN0WVC34reCDlr9Ox0kdI0DTabDePxmMuu6VZthOedhvSBIgd0USSPb7vdPuF903tl6HsptBsKhbiZIOU4tlotQ5WsTyfdUyTBbrdzJVe/3+cGePPMqZpZaIlmoVCNvtPpxHg8RqvVwv7+Pn77298im83y9y4LemWQZRkulwuXLl3C6uoqQqEQx1ZJGZbxkJ5MJggEAvjhD3+I1dVVnD9/Hn6/H9VqFZlMBg8ePMDNmzdRLpcBwBC5H6/K9GZqs9kQDAY59GaxWNDr9VAqlXhIm5Fko78RU14UhaxHoxH8fj9P4lUUBc1mk2/WAPh7AXCCosPhwKVLl+DxeLC2tsb9riwWC1fHJZNJ7O7ucs7aoiaWk/z0Yf9QKIRIJMJGTC6Xw9dff429vT2Uy2W0221DemRoLU0bKq1WC4eHh0gmk2i32ycakVIiOA0SjUaj+JM/+RPEYjG4XC6Uy2U8fvwYn3/+OdLpNHt0jHLGjcdjPrdoeO/GxgYURWGPTCaT4ZYq82JmoSV9fwKbzcZjCfr9PsrlMg4PD09kNC8T9MxUWhwMBjlrnuK0g8FgKXNjCLvdjvX1dcTjcfh8PjidTh7+l81mcXx8zP1Blk1/CNIhTdN4WjE18qKxFkYyhPUHj/6mS1+jkKSmaezBbLVaSCaT6Ha73/LIkPxcLhc++OADHmhrtVr50KZ+PNVqFYVCAcVi8UTO0aJCt2qr1coXTSorrtfr3AmaLlRG9ZpPGzHAszyParWKWq32rdAQnWvUXToWi+HSpUuIxWIYjUZot9vI5/PciNFI86n0IUlVVeFwOOD3++H3+yHLMhfxkLccmN8amWmyL8XS6E96KP2GtKzo3XM0g0qSJAwGA1QqFR6kSd+7bCiKgkAgAL/fz8ZdNpvlcQQ0QBNYPvmQIUxeT9If/ZA/ehlJNuS+pjXicDh4gCPdCul5VVVFNBpFv9/nBoHTpbP0M6lSicILk8kE5XIZtVoNR0dH+PLLL3F8fIxSqcRN4RbVeKZDW1EURCIR7scTiUQ4JJJIJHD//n2k02nDd1t/noHmcDhw/vx5bvhGXiuHw8EhSI/Hg9XVVfj9fsRiMdjtdjx48ADZbBZPnjzB8fExG89G8mRNJhNomoZz585hfX0dgUAAbrebq7xSqRQODw9RKpUAzC9tZKaGDJVkPS9RdZmNmGmPFXX0BcADyhKJBCvDom6a3webzcYDNGksfDabxe7uLjKZDBqNBhuDgHE32hehz0EjQ0bfqIvyRuh7jQAdCmTIKIqCdDrNSb4+n4/3FTqogW+vn+nKE8q7ob+bTCaoVCo4OjrCzs4OvvjiC1QqFS7pXvTE+/F4DFVVsbKywq9wOMyFGIlEAvfu3UOr1TJUoupp6C/ZdFBbrVbE43EoioJ3330X4XAY8Xgc8Xgcbrcb8XgcNpsNbrcb/X4fyWQS9+7dw5MnT5DJZHiNLrKuTENez/X1dR4X43A4UCwWUS6XkU6ncXR0xPmv83r2mQ6NpNJIepFnRlVVeDweXiRGTCR7XShbngZqVqtVjk0vA/oYNJVdUwntaDTiBE+af7PMTCfTT3v06vU6CoUC6vW64dYWGRrU+O7x48eoVqtwu91cRaIoCoe0n9eYbNqQoZYQNFun3W7j6OgIiUQCh4eHKBaL3A/DCCEWk8kEp9OJc+fOYW1tjUNx1FI+lUpxgjNgHEP4eehzrmgPoksUAHz88cdoNptYX1/nUTuUh0VzhSgfjUYZ0CXLCLryPCglgtoTAOD8RTL2KSdtbu9pFj9UkiTOh6HOvTSjglphr66uAgCSyeTSHNYvgjbnarWKZrPJzYTowDaSa/J56OPU1BOFXLjUybfX66FaraJcLnP8dRkhOdFoC7vdjmAwiFAoxMNYc7kcdnd3DXcQ6RtqttttDAYD/O///b+5kCCXy3GYxOFwIBgMcgiO1hj9HP2fVJnUbDbxu9/9DslkEolEAolEgseEkMfmeU3RFgkKnYXDYfzoRz9CLBZDMBiE2WzGrVu3cPv2bdy5cwf5fP65RqAR0XswaQ86f/481tfXsb29zaE4Gk9AHuLJ5Nkomf39feRyOdy+fRt3795Fs9k09J5NQ1v1XuBEIoGdnZ0TgzXnacjN1CNDCjIcDjlGTbdtr9eLSqXC37vM0IdNHhjapOfVTOhtQN8XRd/cjTxU7XYblUoFtVrtW8mHRt0wXgQ9L2285PEkvdGXJdP3G1FG9MwAkMlkYLfbufzT4/FwN9bTqtpMJhN6vR7y+TwajQZ3087lcjwHjmYuLfqhTm0NzGYzT/Om2W76+UI07XoR53K9DrR26Ln1UQIKCVGnY0qRID3qdrucBH54eMjdpKlJo1HXHDkput0ud87Wjxyi/5/3879xQ4ZuPuSupQ+80Whwz4v19XW8//77GI/H+Pzzz3lDNuIH/zLIuJMkidvJ07TiZYKSUwOBAC5fvoytrS2EQiH0+318+eWXyGaz+Oqrr7Czs4Nut2voDfY09OuLus3SkLZisYjxeMxN8Iya36DfJ8iN/fd///e4efPmiXEfGxsbHKokT8T0z5EkiRtQttttHkI6GAxONENb9Bs2PYemaXC73YhGo1hdXYXT6USn00G73UY2m0UqleIbtVGhNTQcDtHtdrGzs8Nd5mn6NV2iyANHn3+r1UK73UYikcBXX32FbDaLzz77DMVikcOSRq/EbTabODw8hMlkQqPRgCzLJwoQzmKdzCy0BIC9MTRXiSw1q9V6ooX4snlk9IllZN22220+kEhWwOJvoK8KeWSotJFKiUejEcrlMt949JusUW89L4PWzGg04lyRarXKOWnTjbiMiN4rNR6PWTdo3k2j0QCAlxoywLPQUi6XQ6vVQj6f5yR7+h6jNKOk/jH6/AY6nBuNBo9Fma7wMiq0hqrVKnK5HHczpkaI0+FISXo2jLXZbCKXyyGRSCCXyyGVSqFSqWAwGBgmj+o09C0JqtUqzGYzj7I4q8vTTHNkqKb84OAAg8GAh3JlMhmk02mUy2VDNlp6GfqExXw+j1/+8pfQNI0t+mw2u3RhE1r8ZPjW63X2UH399dc4Pj5GpVJZqsmyL4L0ZzAYoFAo4G/+5m/gcDj4UH/69KnhKpZOg/JmRqMRT9ytVCpIp9MA8FJDZDQaYTAYYDQaodPpGHI/oosCXSKHwyHS6TTG4zH29vZQqVTw8OFD5HI5rswymgz06C/bT548QSqVwvHxMf7f//t/3Bbjed/f6/XQ7Xa5F1q73f5WKwijyo2M+mazid3dXeTzeXS7Xdjtdt6r9WfXPJlZjgzdCOlDVxQFlUoFLpeLLTmKby8Tem8MxWcfPnzIVi0lRht1MZwGGcCDwYBzYkqlErLZLDKZDHuqjL7Jnsa0/rRaLezs7MBsNp9Yc0Zpi/4ypvOFKEZP+Xmv6pUinaIcEqNCBRfkieh2u3j8+DE3mmw0GhzmN7ruAM/0plgsAnhWebO/v38ipDS9jigXhEK6+guD0WWmN+aKxSJfGqibOKWSGMKQmX6IbreLBw8e4OjoCOl0Gm63G7lcDvl8nhXIyBvHy6C5HuSNMGL/j1eBPCzU4jufz3Ou0O7uLprN5twGkC0C+lyZTqdzwg1u9CZmL4Kel/q8kE69zJjRH0JG9fRRaI2qIsnrORgMkMlk0G632YgBlkN39JcCAOh0OqcOF6WLFiW3LmteJ/BNe5V6vc4pAHR5OAuk0xa5w+H43kH26RbQ+g9/FhtHs9mcm2ZpmvZG5DNtuMzSsm+1WnOTz3fRH72+kM6QjszjxjNP/bHb7W9Uf4hZhkba7fbc5OPxeBYuyadarS7E/jNvvSHmtf+8ztqaHn2h/9o0zzu73pTM5rm2FEV5Y3uP3oM3yz262+2+8AfPrSkC3RiXLffjVdDLQsjlG4QsXg0hJ8H3Ydn157QeQ6f9m1f5PqMzffk+K3nM3JAxetzw+yLkc5IXbRBCRs9H6I/gu2Ckaqw3gVhHr8/bJLPFblMpMCRvy+IQCAQCwdvPqTkyAoFAIBAIBG8zxkzRFwgEAoFAsBQIQ0YgEAgEAsHCIgwZgUAgEAgEC4swZAQCgUAgECwswpARCAQCgUCwsAhDRiAQCAQCwcLy/wGdvUkVHnkhOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "# You do not need to modify anything in this cell\n",
    "\n",
    "m, n = X.shape\n",
    "\n",
    "fig, axes = plt.subplots(8,8, figsize=(8,8))\n",
    "fig.tight_layout(pad=0.1)\n",
    "\n",
    "for i,ax in enumerate(axes.flat):\n",
    "    # Select random indices\n",
    "    random_index = np.random.randint(m)\n",
    "    \n",
    "    # Select rows corresponding to the random indices and\n",
    "    # reshape the image\n",
    "    X_random_reshaped = X[random_index].reshape((20,20)).T\n",
    "    \n",
    "    # Display the image\n",
    "    ax.imshow(X_random_reshaped, cmap='gray')\n",
    "    \n",
    "    # Display the label above the image\n",
    "    ax.set_title(y[random_index,0])\n",
    "    ax.set_axis_off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"2.3\"></a>\n",
    "### 2.3 Model representation\n",
    "\n",
    "The neural network you will use in this assignment is shown in the figure below. \n",
    "- This has three dense layers with sigmoid activations.\n",
    "    - Recall that our inputs are pixel values of digit images.\n",
    "    - Since the images are of size $20\\times20$, this gives us $400$ inputs  \n",
    "    \n",
    "<img src=\"images/C2_W1_Assign1.PNG\" width=\"500\" height=\"400\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The parameters have dimensions that are sized for a neural network with $25$ units in layer 1, $15$ units in layer 2 and $1$ output unit in layer 3. \n",
    "\n",
    "    - Recall that the dimensions of these parameters are determined as follows:\n",
    "        - If network has $s_{in}$ units in a layer and $s_{out}$ units in the next layer, then \n",
    "            - $W$ will be of dimension $s_{in} \\times s_{out}$.\n",
    "            - $b$ will a vector with $s_{out}$ elements\n",
    "  \n",
    "    - Therefore, the shapes of `W`, and `b`,  are \n",
    "        - layer1: The shape of `W1` is (400, 25) and the shape of `b1` is (25,)\n",
    "        - layer2: The shape of `W2` is (25, 15) and the shape of `b2` is: (15,)\n",
    "        - layer3: The shape of `W3` is (15, 1) and the shape of `b3` is: (1,)\n",
    ">**Note:** The bias vector `b` could be represented as a 1-D (n,) or 2-D (n,1) array. Tensorflow utilizes a 1-D representation and this lab will maintain that convention. \n",
    "               "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"2.4\"></a>\n",
    "### 2.4 Tensorflow Model Implementation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tensorflow models are built layer by layer. A layer's input dimensions ($s_{in}$ above) are calculated for you. You specify a layer's *output dimensions* and this determines the next layer's input dimension. The input dimension of the first layer is derived from the size of the input data specified in the `model.fit` statment below. \n",
    ">**Note:** It is also possible to add an input layer that specifies the input dimension of the first layer. For example:  \n",
    "`tf.keras.Input(shape=(400,)),    #specify input shape`  \n",
    "We will include that here to illuminate some model sizing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"ex01\"></a>\n",
    "### Exercise 1\n",
    "\n",
    "Below, using Keras [Sequential model](https://keras.io/guides/sequential_model/) and [Dense Layer](https://keras.io/api/layers/core_layers/dense/) with a sigmoid activation to construct the network described above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# UNQ_C1\n",
    "# GRADED CELL: Sequential model\n",
    "\n",
    "model = Sequential(\n",
    "    [               \n",
    "        tf.keras.Input(shape=(400,)),    #specify input size\n",
    "        ### START CODE HERE ### \n",
    "        tf.keras.layers.Dense(25, activation=\"sigmoid\"),\n",
    "        tf.keras.layers.Dense(15, activation=\"sigmoid\"),\n",
    "        tf.keras.layers.Dense(1, activation=\"sigmoid\")\n",
    "        ### END CODE HERE ### \n",
    "    ], name = \"my_model\" \n",
    ")                            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"my_model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 25)                10025     \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 15)                390       \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 16        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 10,431\n",
      "Trainable params: 10,431\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Expected Output (Click to Expand) </b></font></summary>\n",
    "The `model.summary()` function displays a useful summary of the model. Because we have specified an input layer size, the shape of the weight and bias arrays are determined and the total number of parameters per layer can be shown. Note, the names of the layers may vary as they are auto-generated.  \n",
    "    \n",
    "    \n",
    "```\n",
    "Model: \"my_model\"\n",
    "_________________________________________________________________\n",
    "Layer (type)                 Output Shape              Param #   \n",
    "=================================================================\n",
    "dense (Dense)                (None, 25)                10025     \n",
    "_________________________________________________________________\n",
    "dense_1 (Dense)              (None, 15)                390       \n",
    "_________________________________________________________________\n",
    "dense_2 (Dense)              (None, 1)                 16        \n",
    "=================================================================\n",
    "Total params: 10,431\n",
    "Trainable params: 10,431\n",
    "Non-trainable params: 0\n",
    "_________________________________________________________________\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click for hints</b></font></summary>\n",
    "As described in the lecture:\n",
    "    \n",
    "```python\n",
    "model = Sequential(                      \n",
    "    [                                   \n",
    "        tf.keras.Input(shape=(400,)),    # specify input size (optional)\n",
    "        Dense(25, activation='sigmoid'), \n",
    "        Dense(15, activation='sigmoid'), \n",
    "        Dense(1,  activation='sigmoid')  \n",
    "    ], name = \"my_model\"                                    \n",
    ")                                       \n",
    "``` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92mAll tests passed!\n"
     ]
    }
   ],
   "source": [
    "# UNIT TESTS\n",
    "from public_tests import * \n",
    "\n",
    "test_c1(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The parameter counts shown in the summary correspond to the number of elements in the weight and bias arrays as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "L1 params =  10025 , L2 params =  390 ,  L3 params =  16\n"
     ]
    }
   ],
   "source": [
    "L1_num_params = 400 * 25 + 25  # W1 parameters  + b1 parameters\n",
    "L2_num_params = 25 * 15 + 15   # W2 parameters  + b2 parameters\n",
    "L3_num_params = 15 * 1 + 1     # W3 parameters  + b3 parameters\n",
    "print(\"L1 params = \", L1_num_params, \", L2 params = \", L2_num_params, \",  L3 params = \", L3_num_params )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's further examine the weights to verify that tensorflow produced the same dimensions as we calculated above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "[layer1, layer2, layer3] = model.layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 shape = (400, 25), b1 shape = (25,)\n",
      "W2 shape = (25, 15), b2 shape = (15,)\n",
      "W3 shape = (15, 1), b3 shape = (1,)\n"
     ]
    }
   ],
   "source": [
    "#### Examine Weights shapes\n",
    "W1,b1 = layer1.get_weights()\n",
    "W2,b2 = layer2.get_weights()\n",
    "W3,b3 = layer3.get_weights()\n",
    "print(f\"W1 shape = {W1.shape}, b1 shape = {b1.shape}\")\n",
    "print(f\"W2 shape = {W2.shape}, b2 shape = {b2.shape}\")\n",
    "print(f\"W3 shape = {W3.shape}, b3 shape = {b3.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**\n",
    "```\n",
    "W1 shape = (400, 25), b1 shape = (25,)  \n",
    "W2 shape = (25, 15), b2 shape = (15,)  \n",
    "W3 shape = (15, 1), b3 shape = (1,)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`xx.get_weights` returns a NumPy array. One can also access the weights directly in their tensor form. Note the shape of the tensors in the final layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<tf.Variable 'dense_2/kernel:0' shape=(15, 1) dtype=float32, numpy=\n",
      "array([[-0.08530456],\n",
      "       [-0.2747036 ],\n",
      "       [ 0.08510572],\n",
      "       [-0.12527409],\n",
      "       [-0.2926382 ],\n",
      "       [-0.34840912],\n",
      "       [ 0.21684825],\n",
      "       [-0.08979291],\n",
      "       [ 0.5360281 ],\n",
      "       [ 0.19300771],\n",
      "       [-0.44613487],\n",
      "       [ 0.1397686 ],\n",
      "       [-0.42860353],\n",
      "       [ 0.5345983 ],\n",
      "       [ 0.22546476]], dtype=float32)>, <tf.Variable 'dense_2/bias:0' shape=(1,) dtype=float32, numpy=array([0.], dtype=float32)>]\n"
     ]
    }
   ],
   "source": [
    "print(model.layers[2].weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code will define a loss function and run gradient descent to fit the weights of the model to the training data. This will be explained in more detail in the following week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.6348\n",
      "Epoch 2/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.4996\n",
      "Epoch 3/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.3573\n",
      "Epoch 4/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.2490\n",
      "Epoch 5/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.1787\n",
      "Epoch 6/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.1338\n",
      "Epoch 7/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.1047\n",
      "Epoch 8/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0848\n",
      "Epoch 9/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.0708\n",
      "Epoch 10/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0600\n",
      "Epoch 11/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.0520\n",
      "Epoch 12/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0456\n",
      "Epoch 13/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.0405\n",
      "Epoch 14/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0365\n",
      "Epoch 15/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0332\n",
      "Epoch 16/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.0304\n",
      "Epoch 17/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0280\n",
      "Epoch 18/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.0260\n",
      "Epoch 19/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0243\n",
      "Epoch 20/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.0228\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f2d2b00a650>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(\n",
    "    loss=tf.keras.losses.BinaryCrossentropy(),\n",
    "    optimizer=tf.keras.optimizers.Adam(0.001),\n",
    ")\n",
    "\n",
    "model.fit(\n",
    "    X,y,\n",
    "    epochs=20\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run the model on an example to make a prediction, use [Keras `predict`](https://www.tensorflow.org/api_docs/python/tf/keras/Model). The input to `predict` is an array so the single example is reshaped to be two dimensional."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " predicting a zero: [[0.01574531]]\n",
      " predicting a one:  [[0.98137283]]\n"
     ]
    }
   ],
   "source": [
    "prediction = model.predict(X[0].reshape(1,400))  # a zero\n",
    "print(f\" predicting a zero: {prediction}\")\n",
    "prediction = model.predict(X[500].reshape(1,400))  # a one\n",
    "print(f\" predicting a one:  {prediction}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output of the model is interpreted as a probability. In the first example above, the input is a zero. The model predicts the probability that the input is a one is nearly zero. \n",
    "In the second example, the input is a one. The model predicts the probability that the input is a one is nearly one.\n",
    "As in the case of logistic regression, the probability is compared to a threshold to make a final prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction after threshold: 1\n"
     ]
    }
   ],
   "source": [
    "if prediction >= 0.5:\n",
    "    yhat = 1\n",
    "else:\n",
    "    yhat = 0\n",
    "print(f\"prediction after threshold: {yhat}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the predictions vs the labels for a random sample of 64 digits. This takes a moment to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "# You do not need to modify anything in this cell\n",
    "\n",
    "m, n = X.shape\n",
    "\n",
    "fig, axes = plt.subplots(8,8, figsize=(8,8))\n",
    "fig.tight_layout(pad=0.1,rect=[0, 0.03, 1, 0.92]) #[left, bottom, right, top]\n",
    "\n",
    "for i,ax in enumerate(axes.flat):\n",
    "    # Select random indices\n",
    "    random_index = np.random.randint(m)\n",
    "    \n",
    "    # Select rows corresponding to the random indices and\n",
    "    # reshape the image\n",
    "    X_random_reshaped = X[random_index].reshape((20,20)).T\n",
    "    \n",
    "    # Display the image\n",
    "    ax.imshow(X_random_reshaped, cmap='gray')\n",
    "    \n",
    "    # Predict using the Neural Network\n",
    "    prediction = model.predict(X[random_index].reshape(1,400))\n",
    "    if prediction >= 0.5:\n",
    "        yhat = 1\n",
    "    else:\n",
    "        yhat = 0\n",
    "    \n",
    "    # Display the label above the image\n",
    "    ax.set_title(f\"{y[random_index,0]},{yhat}\")\n",
    "    ax.set_axis_off()\n",
    "fig.suptitle(\"Label, yhat\", fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a name=\"2.5\"></a>\n",
    "### 2.5 NumPy Model Implementation (Forward Prop in NumPy)\n",
    "As described in lecture, it is possible to build your own dense layer using NumPy. This can then be utilized to build a multi-layer neural network. \n",
    "\n",
    "<img src=\"images/C2_W1_dense2.PNG\" width=\"600\" height=\"450\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"ex02\"></a>\n",
    "### Exercise 2\n",
    "\n",
    "Below, build a dense layer subroutine. The example in lecture utilized a for loop to visit each unit (`j`) in the layer and perform the dot product of the weights for that unit (`W[:,j]`) and sum the bias for the unit (`b[j]`) to form `z`. An activation function `g(z)` is then applied to that result. This section will not utilize some of the matrix operations described in the optional lectures. These will be explored in a later section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# UNQ_C2\n",
    "# GRADED FUNCTION: my_dense\n",
    "\n",
    "def my_dense(a_in, W, b, g):\n",
    "    \"\"\"\n",
    "    Computes dense layer\n",
    "    Args:\n",
    "      a_in (ndarray (n, )) : Data, 1 example \n",
    "      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\n",
    "      b    (ndarray (j, )) : bias vector, j units  \n",
    "      g    activation function (e.g. sigmoid, relu..)\n",
    "    Returns\n",
    "      a_out (ndarray (j,))  : j units\n",
    "    \"\"\"\n",
    "    units = W.shape[1]\n",
    "    a_out = np.zeros(units)\n",
    "### START CODE HERE ### \n",
    "    for i in range(units):\n",
    "        w = W[:,i]\n",
    "        z=np.dot(w,a_in) + b[i]\n",
    "        a_out[i]=g(z)\n",
    "### END CODE HERE ### \n",
    "    return(a_out)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.54735762 0.57932425 0.61063923]\n"
     ]
    }
   ],
   "source": [
    "# Quick Check\n",
    "x_tst = 0.1*np.arange(1,3,1).reshape(2,)  # (1 examples, 2 features)\n",
    "W_tst = 0.1*np.arange(1,7,1).reshape(2,3) # (2 input features, 3 output features)\n",
    "b_tst = 0.1*np.arange(1,4,1).reshape(3,)  # (3 features)\n",
    "A_tst = my_dense(x_tst, W_tst, b_tst, sigmoid)\n",
    "print(A_tst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**\n",
    "```\n",
    "[0.54735762 0.57932425 0.61063923]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click for hints</b></font></summary>\n",
    "As described in the lecture:\n",
    "    \n",
    "```python\n",
    "def my_dense(a_in, W, b, g):\n",
    "    \"\"\"\n",
    "    Computes dense layer\n",
    "    Args:\n",
    "      a_in (ndarray (n, )) : Data, 1 example \n",
    "      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\n",
    "      b    (ndarray (j, )) : bias vector, j units  \n",
    "      g    activation function (e.g. sigmoid, relu..)\n",
    "    Returns\n",
    "      a_out (ndarray (j,))  : j units\n",
    "    \"\"\"\n",
    "    units = W.shape[1]\n",
    "    a_out = np.zeros(units)\n",
    "    for j in range(units):             \n",
    "        w =                            # Select weights for unit j. These are in column j of W\n",
    "        z =                            # dot product of w and a_in + b\n",
    "        a_out[j] =                     # apply activation to z\n",
    "    return(a_out)\n",
    "```\n",
    "   \n",
    "    \n",
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click for more hints</b></font></summary>\n",
    "\n",
    "    \n",
    "```python\n",
    "def my_dense(a_in, W, b, g):\n",
    "    \"\"\"\n",
    "    Computes dense layer\n",
    "    Args:\n",
    "      a_in (ndarray (n, )) : Data, 1 example \n",
    "      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\n",
    "      b    (ndarray (j, )) : bias vector, j units  \n",
    "      g    activation function (e.g. sigmoid, relu..)\n",
    "    Returns\n",
    "      a_out (ndarray (j,))  : j units\n",
    "    \"\"\"\n",
    "    units = W.shape[1]\n",
    "    a_out = np.zeros(units)\n",
    "    for j in range(units):             \n",
    "        w = W[:,j]                     \n",
    "        z = np.dot(w, a_in) + b[j]     \n",
    "        a_out[j] = g(z)                \n",
    "    return(a_out)\n",
    "``` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92mAll tests passed!\n"
     ]
    }
   ],
   "source": [
    "# UNIT TESTS\n",
    "test_c2(my_dense)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell builds a three-layer neural network utilizing the `my_dense` subroutine above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_sequential(x, W1, b1, W2, b2, W3, b3):\n",
    "    a1 = my_dense(x,  W1, b1, sigmoid)\n",
    "    a2 = my_dense(a1, W2, b2, sigmoid)\n",
    "    a3 = my_dense(a2, W3, b3, sigmoid)\n",
    "    return(a3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can copy trained weights and biases from Tensorflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "W1_tmp,b1_tmp = layer1.get_weights()\n",
    "W2_tmp,b2_tmp = layer2.get_weights()\n",
    "W3_tmp,b3_tmp = layer3.get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yhat =  0  label=  0\n",
      "yhat =  1  label=  1\n"
     ]
    }
   ],
   "source": [
    "# make predictions\n",
    "prediction = my_sequential(X[0], W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\n",
    "if prediction >= 0.5:\n",
    "    yhat = 1\n",
    "else:\n",
    "    yhat = 0\n",
    "print( \"yhat = \", yhat, \" label= \", y[0,0])\n",
    "prediction = my_sequential(X[500], W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\n",
    "if prediction >= 0.5:\n",
    "    yhat = 1\n",
    "else:\n",
    "    yhat = 0\n",
    "print( \"yhat = \", yhat, \" label= \", y[500,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following cell to see predictions from both the Numpy model and the Tensorflow model. This takes a moment to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "# You do not need to modify anything in this cell\n",
    "\n",
    "m, n = X.shape\n",
    "\n",
    "fig, axes = plt.subplots(8,8, figsize=(8,8))\n",
    "fig.tight_layout(pad=0.1,rect=[0, 0.03, 1, 0.92]) #[left, bottom, right, top]\n",
    "\n",
    "for i,ax in enumerate(axes.flat):\n",
    "    # Select random indices\n",
    "    random_index = np.random.randint(m)\n",
    "    \n",
    "    # Select rows corresponding to the random indices and\n",
    "    # reshape the image\n",
    "    X_random_reshaped = X[random_index].reshape((20,20)).T\n",
    "    \n",
    "    # Display the image\n",
    "    ax.imshow(X_random_reshaped, cmap='gray')\n",
    "\n",
    "    # Predict using the Neural Network implemented in Numpy\n",
    "    my_prediction = my_sequential(X[random_index], W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\n",
    "    my_yhat = int(my_prediction >= 0.5)\n",
    "\n",
    "    # Predict using the Neural Network implemented in Tensorflow\n",
    "    tf_prediction = model.predict(X[random_index].reshape(1,400))\n",
    "    tf_yhat = int(tf_prediction >= 0.5)\n",
    "    \n",
    "    # Display the label above the image\n",
    "    ax.set_title(f\"{y[random_index,0]},{tf_yhat},{my_yhat}\")\n",
    "    ax.set_axis_off() \n",
    "fig.suptitle(\"Label, yhat Tensorflow, yhat Numpy\", fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a name=\"2.6\"></a>\n",
    "### 2.6 Vectorized NumPy Model Implementation (Optional)\n",
    "The optional lectures described vector and matrix operations that can be used to speed the calculations.\n",
    "Below describes a layer operation that computes the output for all units in a layer on a given input example:\n",
    "\n",
    "<img src=\"images/C2_W1_VectorMatrix.PNG\" width=\"600\" height=\"450\">\n",
    "\n",
    "We can demonstrate this using the examples `X` and the `W1`,`b1` parameters above. We use `np.matmul` to perform the matrix multiply. Note, the dimensions of x and W must be compatible as shown in the diagram above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 25)\n"
     ]
    }
   ],
   "source": [
    "x = X[0].reshape(-1,1)         # column vector (400,1)\n",
    "z1 = np.matmul(x.T,W1) + b1    # (1,400)(400,25) = (1,25)\n",
    "a1 = sigmoid(z1)\n",
    "print(a1.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can take this a step further and compute all the units for all examples in one Matrix-Matrix operation.\n",
    "\n",
    "<img src=\"images/C2_W1_MatrixMatrix.PNG\" width=\"600\" height=\"450\">\n",
    "The full operation is $\\mathbf{Z}=\\mathbf{XW}+\\mathbf{b}$. This will utilize NumPy broadcasting to expand $\\mathbf{b}$ to $m$ rows. If this is unfamiliar, a short tutorial is provided at the end of the notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"ex03\"></a>\n",
    "### Exercise 3\n",
    "\n",
    "Below, compose a new `my_dense_v` subroutine that performs the layer calculations for a matrix of examples. This will utilize `np.matmul()`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# UNQ_C3\n",
    "# GRADED FUNCTION: my_dense_v\n",
    "\n",
    "def my_dense_v(A_in, W, b, g):\n",
    "    \"\"\"\n",
    "    Computes dense layer\n",
    "    Args:\n",
    "      A_in (ndarray (m,n)) : Data, m examples, n features each\n",
    "      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\n",
    "      b    (ndarray (1,j)) : bias vector, j units  \n",
    "      g    activation function (e.g. sigmoid, relu..)\n",
    "    Returns\n",
    "      A_out (ndarray (m,j)) : m examples, j units\n",
    "    \"\"\"\n",
    "### START CODE HERE ### \n",
    "    A_out = g(np.matmul(A_in,W) + b)\n",
    "    \n",
    "### END CODE HERE ### \n",
    "    return(A_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[0.54735762 0.57932425 0.61063923]\n",
      " [0.57199613 0.61301418 0.65248946]\n",
      " [0.5962827  0.64565631 0.6921095 ]\n",
      " [0.62010643 0.67699586 0.72908792]], shape=(4, 3), dtype=float64)\n"
     ]
    }
   ],
   "source": [
    "X_tst = 0.1*np.arange(1,9,1).reshape(4,2) # (4 examples, 2 features)\n",
    "W_tst = 0.1*np.arange(1,7,1).reshape(2,3) # (2 input features, 3 output features)\n",
    "b_tst = 0.1*np.arange(1,4,1).reshape(1,3) # (1, 3 features)\n",
    "A_tst = my_dense_v(X_tst, W_tst, b_tst, sigmoid)\n",
    "print(A_tst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**\n",
    "\n",
    "```\n",
    "[[0.54735762 0.57932425 0.61063923]\n",
    " [0.57199613 0.61301418 0.65248946]\n",
    " [0.5962827  0.64565631 0.6921095 ]\n",
    " [0.62010643 0.67699586 0.72908792]]\n",
    " ```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click for hints</b></font></summary>\n",
    "    In matrix form, this can be written in one or two lines. \n",
    "    \n",
    "       Z = np.matmul of A_in and W plus b    \n",
    "       A_out is g(Z)  \n",
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click for code</b></font></summary>\n",
    "\n",
    "```python\n",
    "def my_dense_v(A_in, W, b, g):\n",
    "    \"\"\"\n",
    "    Computes dense layer\n",
    "    Args:\n",
    "      A_in (ndarray (m,n)) : Data, m examples, n features each\n",
    "      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\n",
    "      b    (ndarray (j,1)) : bias vector, j units  \n",
    "      g    activation function (e.g. sigmoid, relu..)\n",
    "    Returns\n",
    "      A_out (ndarray (m,j)) : m examples, j units\n",
    "    \"\"\"\n",
    "    Z = np.matmul(A_in,W) + b    \n",
    "    A_out = g(Z)                 \n",
    "    return(A_out)\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92mAll tests passed!\n"
     ]
    }
   ],
   "source": [
    "# UNIT TESTS\n",
    "test_c3(my_dense_v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell builds a three-layer neural network utilizing the `my_dense_v` subroutine above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_sequential_v(X, W1, b1, W2, b2, W3, b3):\n",
    "    A1 = my_dense_v(X,  W1, b1, sigmoid)\n",
    "    A2 = my_dense_v(A1, W2, b2, sigmoid)\n",
    "    A3 = my_dense_v(A2, W3, b3, sigmoid)\n",
    "    return(A3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can again copy trained weights and biases from Tensorflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "W1_tmp,b1_tmp = layer1.get_weights()\n",
    "W2_tmp,b2_tmp = layer2.get_weights()\n",
    "W3_tmp,b3_tmp = layer3.get_weights()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make a prediction with the new model. This will make a prediction on *all of the examples at once*. Note the shape of the output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([1000, 1])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Prediction = my_sequential_v(X, W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\n",
    "Prediction.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll apply a threshold of 0.5 as before, but to all predictions at once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predict a zero:  [0] predict a one:  [1]\n"
     ]
    }
   ],
   "source": [
    "Yhat = (Prediction >= 0.5).numpy().astype(int)\n",
    "print(\"predict a zero: \",Yhat[0], \"predict a one: \", Yhat[500])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following cell to see predictions. This will use the predictions we just calculated above. This takes a moment to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "# You do not need to modify anything in this cell\n",
    "\n",
    "m, n = X.shape\n",
    "\n",
    "fig, axes = plt.subplots(8, 8, figsize=(8, 8))\n",
    "fig.tight_layout(pad=0.1, rect=[0, 0.03, 1, 0.92]) #[left, bottom, right, top]\n",
    "\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    # Select random indices\n",
    "    random_index = np.random.randint(m)\n",
    "    \n",
    "    # Select rows corresponding to the random indices and\n",
    "    # reshape the image\n",
    "    X_random_reshaped = X[random_index].reshape((20, 20)).T\n",
    "    \n",
    "    # Display the image\n",
    "    ax.imshow(X_random_reshaped, cmap='gray')\n",
    "   \n",
    "    # Display the label above the image\n",
    "    ax.set_title(f\"{y[random_index,0]}, {Yhat[random_index, 0]}\")\n",
    "    ax.set_axis_off() \n",
    "fig.suptitle(\"Label, Yhat\", fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see how one of the misclassified images looks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 72x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(1, 1))\n",
    "errors = np.where(y != Yhat)\n",
    "random_index = errors[0][0]\n",
    "X_random_reshaped = X[random_index].reshape((20, 20)).T\n",
    "plt.imshow(X_random_reshaped, cmap='gray')\n",
    "plt.title(f\"{y[random_index,0]}, {Yhat[random_index, 0]}\")\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"2.7\"></a>\n",
    "### 2.7 Congratulations!\n",
    "You have successfully built and utilized a neural network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a name=\"2.8\"></a>\n",
    "### 2.8 NumPy Broadcasting Tutorial (Optional)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
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   "source": [
    "In the last example,  $\\mathbf{Z}=\\mathbf{XW} + \\mathbf{b}$ utilized NumPy broadcasting to expand the vector $\\mathbf{b}$. If you are not familiar with NumPy Broadcasting, this short tutorial is provided.\n",
    "\n",
    "$\\mathbf{XW}$  is a matrix-matrix operation with dimensions $(m,j_1)(j_1,j_2)$ which results in a matrix with dimension  $(m,j_2)$. To that, we add a vector $\\mathbf{b}$ with dimension $(1,j_2)$.  $\\mathbf{b}$ must be expanded to be a $(m,j_2)$ matrix for this element-wise operation to make sense. This expansion is accomplished for you by NumPy broadcasting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Broadcasting applies to element-wise operations.  \n",
    "Its basic operation is to 'stretch' a smaller dimension by replicating elements to match a larger dimension.\n",
    "\n",
    "More [specifically](https://NumPy.org/doc/stable/user/basics.broadcasting.html): \n",
    "When operating on two arrays, NumPy compares their shapes element-wise. It starts with the trailing (i.e. rightmost) dimensions and works its way left. Two dimensions are compatible when\n",
    "- they are equal, or\n",
    "- one of them is 1   \n",
    "\n",
    "If these conditions are not met, a ValueError: operands could not be broadcast together exception is thrown, indicating that the arrays have incompatible shapes. The size of the resulting array is the size that is not 1 along each axis of the inputs.\n",
    "\n",
    "Here are some examples:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<figure>\n",
    "    <center> <img src=\"./images/C2_W1_Assign1_BroadcastIndexes.PNG\"  alt='missing' width=\"400\"  ><center/>\n",
    "    <figcaption>Calculating Broadcast Result shape</figcaption>\n",
    "<figure/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graphic below describes expanding dimensions. Note the red text below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<figure>\n",
    "    <center> <img src=\"./images/C2_W1_Assign1_Broadcasting.gif\"  alt='missing' width=\"600\"  ><center/>\n",
    "    <figcaption>Broadcast notionally expands arguments to match for element wise operations</figcaption>\n",
    "<figure/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graphic above shows NumPy expanding the arguments to match before the final operation. Note that this is a notional description. The actual mechanics of NumPy operation choose the most efficient implementation.\n",
    "\n",
    "For each of the following examples, try to guess the size of the result before running the example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(a + b).shape: (3, 1), \n",
      "a + b = \n",
      "[[6]\n",
      " [7]\n",
      " [8]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1,2,3]).reshape(-1,1)  #(3,1)\n",
    "b = 5\n",
    "print(f\"(a + b).shape: {(a + b).shape}, \\na + b = \\n{a + b}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this applies to all element-wise operations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(a * b).shape: (3, 1), \n",
      "a * b = \n",
      "[[ 5]\n",
      " [10]\n",
      " [15]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1,2,3]).reshape(-1,1)  #(3,1)\n",
    "b = 5\n",
    "print(f\"(a * b).shape: {(a * b).shape}, \\na * b = \\n{a * b}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<figure>\n",
    "    <img src=\"./images/C2_W1_Assign1_VectorAdd.PNG\"  alt='missing' width=\"740\" >\n",
    "    <center><figcaption><b>Row-Column Element-Wise Operations</b></figcaption></center>\n",
    "<figure/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1]\n",
      " [2]\n",
      " [3]\n",
      " [4]]\n",
      "[[1 2 3]]\n",
      "(a + b).shape: (4, 3), \n",
      "a + b = \n",
      "[[2 3 4]\n",
      " [3 4 5]\n",
      " [4 5 6]\n",
      " [5 6 7]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1,2,3,4]).reshape(-1,1)\n",
    "b = np.array([1,2,3]).reshape(1,-1)\n",
    "print(a)\n",
    "print(b)\n",
    "print(f\"(a + b).shape: {(a + b).shape}, \\na + b = \\n{a + b}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the scenario in the dense layer you built above. Adding a 1-D vector $b$ to a (m,j) matrix.\n",
    "<figure>\n",
    "    <img src=\"./images/C2_W1_Assign1_BroadcastMatrix.PNG\"  alt='missing' width=\"740\" >\n",
    "    <center><figcaption><b>Matrix + 1-D Vector</b></figcaption></center>\n",
    "<figure/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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